Efficient Hardware-Based Image Compression Schemes for Wireless ...

Wireless Pers Commun DOI 10.1007/s11277-013-1588-8

Efficient Hardware-Based Image Compression Schemes for Wireless Sensor Networks: A Survey Khamees Khalaf Hasan · Umi Kalthum Ngah · Mohd Fadzli Mohd Salleh

© Springer Science+Business Media New York 2014

Abstract Multidimensional sensors, such as digital camera sensors in the visual sensor networks VSNs generate a huge amount of information compared with the scalar sensors in the wireless sensor networks WSNs. Processing and transmitting such data from low power sensor nodes is a challenging issue through their limited computational and restricted bandwidth requirements in a hardware constrained environment. Source coding can be used to reduce the size of vision data collected by the sensor nodes before sending it to its destination. With image compression, a more efficient method of processing and transmission can be obtained by removing the redundant information from the captured image raw data. In this paper, a survey of the main types of the conventional state of the art image compression standards such as JPEG and JPEG2000 is provided. A literature review of their advantages and shortcomings of the application of these algorithms in the VSN hardware environment is specified. Moreover, the main factors influencing the design of compression algorithms in the context of VSN are presented. The selected compression algorithm may have some hardwareoriented properties such as; simplicity in coding, low memory need, low computational load, and high-compression rate. In this survey paper, an energy efficient hardware based image compression is highly requested to counter the severe hardware constraints in the WSNs. Keywords Image compression · Lifting scheme · Set-partitioning in hierarchical trees (SPIHT) · Wireless sensor networks (WSNs) · Visual sensor networks (VSNs)

K. K. Hasan (B) · U. K. Ngah · M. F. M. Salleh School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia e-mail: [email protected] U. K. Ngah e-mail: [email protected] M. F. M. Salleh e-mail: [email protected]

123

K. K. Hasan et al.

1 Introduction Nowadays, the growth in the wireless technology has experienced an exponential curve. These technological advances enable the production of low cost and low power micro-sensing devices with on-board processing and communication capabilities [1–3]. These resourceslimited sensors can build a wireless sensor network (WSN). The WSN is a network which consists of these tiny sensing devices that are densely deployed over large expanses of space. This is to enable harvesting data from the physical phenomenon, performing simple processing at the sensor nodes and transmitting their observed values to some processing or control center called sink node [4–6]. In the wide range applications of WSN, the sensor nodes are spread in certain environments where it is difficult to access and limited power supplied by small irreplaceable batteries. Therefore, sensor node devices can only carry a limited amount of data packets in their lifetime. Under such energy restriction and limitations, both energy consumption and data transmission challenges are always considered together in WSNs [7]. The more general perspective on visual surveillance for sensor networks is not in the scope of this survey paper. In the literature, other relevant previous survey papers offer a specific VSN topic. These papers were written on different aspects of VSN such as (challenging issues [8], general overview [11], multimedia streaming [12,14,16–18], platform design [15]). The main motivations of this survey are two goals. The primary goal of this paper is to compare the VSN platforms to WSN based on their proper processing techniques. The technological challenges include many VSN platform designs are usually affected by hardware limitations and energy constraints. The second one is to introduce some necessary background to the main types of the state of the art image compression algorithms, especially those for sensor network requirements to follow the paper [18–20]. The major differences between visual sensor nodes and scalar sensor nodes are in acquiring, processing, and transferring data. The conventional wireless sensor nodes work on one dimensional scalar data such as temperature, pressure, vibration, and humidity. These sensors are improper for many applications such as environmental monitoring, video surveillance and habitat surveys. The increasing interest and more attention have arisen for ubiquitous applications requiring vision capabilities. Therefore, there has arisen a demand for WSNs with multidimensional data sensors, such as low-cost digital imaging cameras to build visual sensor networks VSNs. Such sensors can be developed as an upgrade of the classic scalar wireless sensor networks. The VSNs require both higher processing power and communication bandwidth [8,11–14]. The visual sensors VSs equipped with limited memory capabilities. Therefore, limited memory has become another major constraint in the processing of bulky data images [2,8–11]. Therefore, increasing the network lifetime and decreasing energy consumption has been always optimization objectives. As a consequence of the limited resources and restricted processing capability, useful and an efficient exploit of data processing and communication techniques have been designed for the VSNs. Among the proposed techniques is the image compression design which can be used to decrease the transmitted data size over wireless channels. The image captured by the VS is compressed by exploiting data correlation and redundancy from the raw data before it is transmitted [12,15–17]. While scalar sensor networks can depend on redundant sensor readings during spatial redundancy in the distribution of sensor nodes, this is not a practical solution for VSNs, which has to cope with larger traffic data. VSNs can use source coding in order to reduce the amount of redundant data captured by visual sensor nodes before sending it to its destination.

123

Efficient Hardware-Based Image Compression Schemes

Generally, the data processing consumes much less energy than transmitting the data in a wireless medium. Therefore, using the compression process before transmitting the data will reduce the total energy consumption by a sensor node. It is possible for data compression to be maintained at a high compression ratio with noticeable degradation in the quality of reconstructed images. Because of this fact, the complication of the image compression process becomes very challenging in the VSNs context [8,18,19]. The conventional compression standards involved such as JPEG and JPEG2000 are not applicable for resource restricted VSNs. These algorithms need complex hardware requirements and make the wireless communication energy dissipation compared to uncompressed image transmitting. The selected compression algorithm could have some parameters such as; simplicity in coding, low memory need, low computational load, and high-compression rate. According to these guidelines, we conclude a compression method for the WSNs. The remaining sections of this report are organized in the following manner. Section 2, presents an overview of the two basic image coding and computation techniques. The DCT and the DWT based image compression algorithms such as Embedded Zerotree Wavelet (EZW), Set Partitioning in Hierarchical Trees (SPIHT) and Embedded Bock Coding with Optimized Truncation (EBCOT) algorithms are briefly introduced. Then, the data compression algorithms in WSNs and its two principal categories: local data compression approach and distributed data compression approach are explained in detail in Sect. 3. Next, the hardware architecture of a typical sensor node in the WSN platforms is elaborated in Sect. 4. Section 5, presents some comparison and survey analysis. Finally, Sect. 6 concludes the paper.

2 Basic Image Coding and Computation Techniques As the neighboring pixels in an image are highly correlated; these redundant details can be discarded by finding a less correlated representation of the image. This is the basic idea behind the image compression theory [19,54]. In this section, we provided an overview of the currently proposed compression schemes of still-image compression algorithms for sensor networks and pointed their advantages and weaknesses. The transform-based image compression methods are still very attractive and popular. These methods are mainly based either on Discrete Cosine Transform DCT such as the Joint Photographic Experts Group JPEG or the low complexity embedded DWT-based coders. These coders are the Embedded Zerotree Wavelet EZW [55], Set-Partitioning in Hierarchical Trees SPIHT [56] and Embedded Block Coding with Optimized Truncation EBCOT [57] algorithms. The widespread image compression standard JPEG2000 adopted the EBCOT image codec algorithm [19,58]. However, they are described under the first generation image coding. While the second generation image coding consists of pyramidal coding, directional decomposition based coding, segmentation based coding and vector quantization. The main disadvantage of the second generation image coding is related to the entropy coding process which is extremely computationally intensive and time consuming. This property makes its implementation in the hardware-oriented environment not feasible. In comparison, first generation image coding performs DCT or DWT to decompose an image into a domain that is more suitable for compression, especially for sensor networks. Taxonomy of the main types of still-image compression algorithms is shown in Fig. 1 [19,54]. The basic components of an image coding process are performed in two stages, namely the image transformation stage followed by the entropy coding stage as indicated in Fig. 2 [19,54].

123

K. K. Hasan et al.

Fig. 1 Taxonomy of the main types of still-image compression algorithms

Fig. 2 The process of image coding [19]

2.1 Discrete Cosine Transform (DCT) Based Image Compression The most popular international standard for lossy and lossless image compression is the JPEG. In lossy JPEG, an N × N image is divided into small individual 8 × 8 image blocks and then a discrete cosine transform is performed on each block. DCT was used because it has properties such as good energy compaction and excellent decorrelation as shown in Fig. 3. DCT in general does not compress the image because it is only a form of transform coding rather than a complete encoder. JPEG uses quantization to discard the transformed coefficients with the least information [53]. Quantization makes use of the fact that the higher frequency components are less important than low frequency components. It allows varying levels of image compression and quality through selection of specific quantization matrices. Zigzag scanning is then applied to rearrange the coefficients prior to entropy coding. The quantized coefficients are encoded using a combination of run length and Huffman coding. DCT based methods image compression algorithms have many high speed with lowcomplexity and low-memory but often cause often causes blocking artifacts that lead to a degradation in performance especially in low bit rates [53,58]. Numerous works on techniques

123

Efficient Hardware-Based Image Compression Schemes

Fig. 3 Energy compaction property of DCT blocks [19]

to reduce the computation cost of the DCT transform are such as by Loeffler et al. [59], Fieg and Winograd [60] and Hyeonuk et al. [61]. Block-based coding is widely adopted in image/video coding systems, e.g., JPEG, MPEG1/2/4, H.261/3/4. In those systems, an image is divided into nonoverlapping blocks that limits the complexity of processing the whole image. For each block the 2-D DCT and entropy coding are applied separately to exploit the correlation within the block. Although DCTbased image compression provides satisfactory compression efficiency and it gives a low memory implementation, the tiling of the blocks causes the notorious blocking artifacts. The other disadvantage is that the transform can only exploit the correlations within the block, which affects the entire system coding efficiency [53,58,104]. One way to resolve the problem is to allow the transform to be performed across different blocks. An example is the discrete wavelet transform DWT used in JPEG 2000, which is applied on the whole image instead of on a block basis. For DWT coding, the transform coefficients of the full image need to be buffered in high precision, thus making memory requirements a bottleneck for hardware implementation. To avoid this problem, an image is divided into strips which are encoded separately. These low memory DWT structures apply the line based [65,73] and strip based image coding [71,72]. The blocking effect or blackness refers to a block pattern in the compressed sequence. It is due to the independent quantization of individual blocks (usually of 8 × 8 pixels in size) in block-based DCT coding schemes, leading to discontinuities at the boundaries of adjacent blocks [105]. While some of these effects are unique to block-based coding schemes, many of them are observed with other compression algorithms as well. In wavelet-based compression, for example, the transform is applied to the entire image, therefore none of the block-related artifacts occur. Instead, blur and ringing are the most prominent distortions [105]. 2.2 Embedded Zerotree Wavelet (EZW) Image Coding Embedded zerotree wavelet (EZW) coding [55], was introduced by Shapiro. It is a remarkably effective, fast in execution and computationally simple technique for embedding bit stream wavelet based image compression. The basic idea behind the EZW is to form a tree structure with its root located into the lowest-frequency subband after the DWT is applied to the image.

123

K. K. Hasan et al.

Fig. 4 The EZW process of encoding a wavelet coefficient

These coefficients usually have a much larger magnitude than the coefficients at the highfrequency subbands. During the coding process of EZW as illustrated in Fig. 4, the coefficient will be compared to a predefined threshold T with a specific scanning order. The process starts at the lowest-frequency subband path during the dominant pass and the subordinate pass. In the dominant pass, when the coefficient magnitude is larger than the threshold, the coefficient is considered as significant. If differently, it is considered as insignificant. If the root is insignificant, all the descendants are also insignificant. In this situation, the complete tree can be coded using a single symbol to achieve compression and is represented as the zerotree root (ZTR) symbol. In another way, the root is regarded as significant and it will be represented by either one POS symbol or one NEG symbol, depending on whether the value is positive or negative. When the root is considered as insignificant but has a significant descendant, the tree is represented by the isolated zero (IZ) symbol. In the subordinate pass, coefficients are tested to determine the significant ones that will be refined to an additional bit of precision. When all the coefficients are refined, the threshold T is halved and the coding process will be repeated. The process will continue until the wanted bit rates are reached. The encoded symbols stream that contains a combination of four symbols (POS, NEG, ZTR and IZ) and the refinement bit ‘1’ or ‘0’ is then arithmetically coded for bit stream transmission [19,55,63]. 2.3 Set-Partitioning in Hierarchical Trees (SPIHT) Image Coding SPIHT algorithm [56] uses the principles of set partitioning by significance; the significance test result is binary ordered bit plane transmission with respect to a sequence of thresholds. In the SPIHT, spatial orientation tree (SOT) arrangement is used to connect the coefficients after the (DWT) is applied to decompose an image into different subbands. In addition, there are two types of descendant trees i.e. Type A set which holds the set of coordinates of all descendants of a node (i, j) also called D(i, j) set and Type B set which holds the set of coordinates of all grand descendants of a node (i, j) known as L(i, j) [57]. In the practical implementation of the SPIHT algorithm, there are two coding passes, the sorting pass and the refinement pass. The order of subsets which are tested for significance is stored in three

123

Efficient Hardware-Based Image Compression Schemes

ordered lists: list of insignificant sets (LIS), list of insignificant pixels (LIP) and a list of significant pixels (LSP). In all the lists, each entry is identified by a coordinate (i, j), which in the LIP and LSP represents individual pixels and in the LIS represents, either the set A or B [56,62]. An important difference between EZW and SPHIT is in their set partitioning rules, where coefficient-trees are partitioned in a more efficient way. During the sorting pass, a significance test is performed on (LIP) elements and those that become significant are moved to the LSP. Sets are sequentially evaluated by significance test following the LIS order. When a set is found to be significant, it is removed from the list and partitioned into four single elements that are added back to the LIS. The new subsets with more than one element are tested and added to the end of the LIP or the LSP, depending on whether they are insignificant or significant, respectively. Refinement coding pass, is then implemented on every coefficient that is added to the LSP except for those that are just added during the sorting pass. Each of the coefficients in the LSP is refined to an additional bit of precision. Lastly, the threshold is halved and SPIHT coding is reiterated until all the wavelet coefficients are coded. The fulfillment of these principles in matched coding and decoding algorithms is shown to be more effective than in the previous implementations of EZW coding [56,62]. 2.4 Embedded Block Coding with Optimized Truncation (EBCOT) Image Coding EBCOT [57] is the main entropy coding algorithm in JPEG2000; the embedded wavelet coding is carried out on a block-based basis. A scalar quantization technique is applied on the wavelet coefficients whereby the quantization step size can also be adjusted. The entropy coding and the generation of compressed bit stream in JPEG2000 through EBCOT are divided into two coding steps: Tier-l and Tier-2 coding as shown in Fig. 5. In Tier 1 coding, the wavelet-transformed image is divided into bit-plane data blocks of samples known as code blocks and encoded independently with an adaptive arithmetic coding [58]. An independently embedded bit-stream is then generated for every code-block. In Tier 2 coding process, these compressed bit-streams are subdivided and packed into different

Fig. 5 Two tiered coding in EBCOT

123

K. K. Hasan et al.

quality layers creating resolution and signal to noise ratio scalable compressed bit-stream for transmission. Depending on the rate-distortion ratio, the length of each encoded bitstream from each code-block can be varied and truncated through the rate control module [63]. EBCOT algorithm supports both lossy and lossless coding in the same framework [57,63]. While EBCOT has the best compression rate of all and is included by JPEG2000, it requires multiple complex coding procedures, extensive rate distortion optimizers and adaptive arithmetic coding techniques [57]. In the following section, we present the data compression algorithms, that is, local data compression approach and distributed data compression approach, which represents a promising technique for WSNs image compression.

3 Data Compression Algorithms in WSNs Several data compression algorithms have been proposed in the literature. There are possible methods to condense resource restrict of wireless sensor nodes. Work in data compression for sensor networks such as Kimura and Latifi, contained only five different types of data compression algorithms. These are not up-to-date and not practical to be implemented in WSNs [18]. Set-Partitioning in Hierarchical Trees (SPIHT) wavelet-based image compression is the most suitable for implementation in a wireless sensor network amongst the eight popular image compression algorithms [19]. Various modifications must therefore be done on the conventional SPIHT for suitable implementation in a hardware constrained environment in WSNs because of the extensive memory requirement needed. Performance comparison of the in-network processing schemes, introduced to achieve sufficient power savings by reducing the amount of data to be transmitted can be achieved through data compression or aggregation techniques [20]. Those five techniques that are analyzed and classified are string compression, image compression, distributed source coding, compressed sensing and data aggregation. Although comparisons of these five data compression schemes are also studied, there was no mention of suitable approaches to overcome shortcomings. Work on analyzing and comparing details, such as performance, suitable applications and limitations of recently developed schemes data compression approaches have tabled the benefits and drawbacks of each scheme in different ways [21]. The findings indicate that, no data compression approach is suitable for all WSNs. The results show that the effectiveness of data compression algorithms for a particular application is still an open issue requiring further investigation especially ones involving WSNs. Data compression algorithms in a wireless sensor network are classified into two main categories: local data compression approach and distributed data compression approach as depicted in Fig. 6. 3.1 Local Data Processing and Compression Scheme Survey The local data processing technique is a universal data compression approach. In this technique, a single source coding is implemented where each individual node compresses its information independent of other sources. The lossless data compression approach was proposed for energy-efficient data gathering in WSNs and temporal correlation in real-world environmental monitoring has discovered [22]. The optimization of the two-mode transmission TMT data compression scheme by arithmetic encoding at the sink node was also done [23]. The computation energy cost is less than 1/10 of the communication energy by using the

123

Efficient Hardware-Based Image Compression Schemes Fig. 6 Arrangement of data compression classification in WSNs [21]

TMT compression scheme in a 1-hop transmission. Marcelloni and Vecchio, [24] proposed a lossy compression approach on limited resources single node based upon predictive coding. Differential pulse code modulation DPCM optimization approach was applied in their work. Also, low computation was used for reducing the data encoded/decoded by a sensor node to prolong its lifetime. Some works [25–28] have shown that popular algorithms such as Joint Photographic Experts Group JPEG, JPEG2000 or traditional Set Partitioning in Hierarchical Trees SPIHT is generally not efficient in software implementations because they lead to greater energy consumption than the transmission of the uncompressed image. This is due to the resource limitation of the software-based platforms. Since a considerable amount of energy in sensor networks is consumed for data transmission, energy efficient compression techniques may prolong the life of such networks. A platform to evaluate the performance of five different traditional algorithms for image compression in a single sensor node was presented [25]. Here, well known algorithms JPEG2000, Sub Sampling (SS), Discrete Cosine Transform (DCT), SPIHT and JPEG were analyzed. Results showed that SS is the unique simplest implementation algorithm and presents energy savings with respect to the no compression case, allowing a power reduction of about 29 % for the considered application. Then, Duran-Faundez and Lecuire propose, a low-complexity pixel interleaving application for robust image transmission over WSNs [26]. Neighboring pixels are transmitted in different packets based on Torus Automorphisms (TA) technique. Results showed that TA does not increase energy consumption and execution time on a wireless camera node, while the quality of the transmitted images is increased with high loss rates. Wu and Chang proposed a scheme which used multiple trees according to parent–child relationship wavelet coefficient decomposition [27]. They then encode them separately by the SPIHT algorithm to form multiple bit streams to reduce error propagation in transmission. Subsequently, the multiple bit streams are divided into small fragments and transmit them in bursts to achieve efficient transmission. Results showed that the proposed scheme has not only high energy efficiency in the transmission but also achieved minimum degradation in reconstructing image quality at the base station over a wide range of channel bit error rates BERs. Huaming and Abouzied proposed a power aware technique that integrates the local compression in a heuristic algorithm called MTE (Minimize Total Energy) under given network conditions and image quality constraints [28]. MTE algorithm tries to minimize the total energy dissipation by selecting the optimal image compression. The algorithm mainly focuses on power efficient techniques for individual components and several extensions of the problem and could not provide an optimized energy performance tradeoff in the image transmission and video coding over WSN.

123

K. K. Hasan et al.

The idea of minimizing the processing and transmission energy when executing JPEG in VSN was investigated by Mammeri et al. [29], whereby the DCT energy compaction used a portion of each block of 8 × 8 DCT coefficients. The selected coefficients portion is triangular (T-JPEG). The work merely discussed and did not take into account the complexity of implementation. Mammeri et al. also addressed the problem of modeling and adapting JPEG to the energy requirements of Visual Sensor Networks and used the square type of the selected coefficients portion called Squared JPEG (S-JPEG) [30]. Although the idea was discussed, there was no mention of how the values of the coefficients are selected. In the case of triangular selection (T-JPEG) Mammeri et al. [31] proposed two methods. The squared approach was however not investigated. Makkaui et al. [32] used both squared and triangular type of the fast zonal DCT for Wireless Camera Sensor Networks. An efficient tuning of the trade-off between energy consumption and image distortion was found to maintain a long network lifetime. However, only the software implementation was done, whereas the hardware performance evaluation has not been performed. 3.2 Distributed Data Processing and Compression Scheme Survey The distributed data compression technique is based on the fact that an individual node distributes the computational task among other nodes. In this case, a distributed method to split the processing task of the compressed image is required. Distributed data processing and compression approaches in WSNs are commonly implemented in dense sensor networks. A distributed approach can be widely classified into four major techniques as depicted in Fig. 6. These techniques are distributed source modelling DSM, distributed transform coding DTC, distributed source coding DSC and compressed sensing CS techniques. This classification is similar to the one determined by Marcelloni and Vecchio [24]. The overall technique is briefly discussed in [21]. Several distributed data compression works have proposed in this study. These focus on the correlation between sensor nodes. The latest CS model combines both signal acquisition and compression. A distributed image compression for images captured by sensor nodes is done through exploiting the correlation in neighboring sensor images having overlapping fields of view is used [33]. The approach uses a technique similar to stereo-image compression to identify overlaps in the images of neighboring sensor nodes was proposed by Boulgouris and Strintzis [34]. Distributed image compression using the JPEG2000 standard is proposed by Wu et al. [35] by sharing the processing of tasks (workload) between various nodes. Results showed that the proposed scheme extends the system lifetime at normalized total energy consumption comparable to the centralized image compression. Wang et al. [36] proposed a technique to optimize system energy by parallelizing computation through the network. By finding the optimal operating points that lead to reduced energy dissipation of the nodes, the end result is a 60 % energy reduction for a sensor application of source localization. Pradhan et al. [37] presented a fast error-correcting coding which is highly effective and efficient compression algorithm, called the distributed source coding using syndromes (DISCUS). This framework enables highly effective and efficient compression across a sensor network. Lu et al. [38] proposed a distributed implementation scheme of the Lapped Biorthogonal Transform (LBT) based on a clustering architecture. They overcome the computation and energy limitation of individual nodes by sharing the processing of tasks. Nasri et al. [39,40] studied the design and evaluation of distributed scheme JPEG2000 image compression algorithm and its application in WSNs. The performance of the proposed

123

Efficient Hardware-Based Image Compression Schemes

image compression scheme is investigated with respect to image quality and energy consumption. Results demonstrated that the proposed scheme analyzed the functional influence of each parameter of this distributed image compression algorithm at different process levels. Nasri et al. again proposed an image transport scheme for wireless sensor networks called skipped high-pass sub-band (SHPS) technique which is based on the Cohen–Daubechies Feauveau (CDF 9/7 DWT) to achieve higher energy efficiency in WSN image transmission depending on the choice of compression levels [41,42]. Experimental results showed that this scheme could save significant 25 % reductions in computational energy compared to a CDF 9/7 DWT with minimal degradation of the image quality. Nguyen et al. proposed distributed image compression algorithm which uses a Lapped Transform technique by coordinating the processing of tasks [43]. In the proposed scheme, the compression process is divided into several small processing components, which are then distributed to multiple nodes while considering their residual energy. The proposed algorithm also minimizes blocked noise in image compression. Dong et al. [44] proposed a method to minimize energy cost and maximize compression in WSN by using simple wavelet distributed compression for WSNs image capture in slow motion scenario. The authors also proposed a change detection algorithm to mark active blocks and only encode these active regions to save energy. The algorithm was also used to reduce computation complexity without sacrificing the image reconstruction quality. Jamali et al. [45] proposed an efficient compression method based on the Distributed Source Coding (DSC) on high correlation sensor nodes. This method consists of two phases: the Training Phase and the Main Phase. The proposed method shows its notability in terms of compression ratio as the correlation increases compared with JPEG. Other studies have also focused on image processing techniques in wireless sensor networks. By mapping individual sensors as pixels in an image, Devaguptapu and Krishnamachari, examined the removal of uncorrelated sensor noise and the decentralized detection of edges [46]. On the other hand Ganesan et al. [47] proposed a generalized hierarchical architecture for multiresolution querying of regularly placed sensor networks on a wide-area precipitation sensor dataset. Servetto et al. [48] also exploited wavelet transforms to decorrelate sensor data to address the sensor broadcast problem where every sensor observes only one pixel. After the detailed survey of the main image compression schemes, we review in Sect. 4 another category of the general hardware architecture of the sensor device.

4 General Hardware Architectures for WSN The general hardware architecture of a typical sensor node as most often assumed in the literature is shown in Fig. 7 [50]. It consists of four main components; a sensing unit including sensors and analog-to-digital converters (ADCs) for data acquisition, a processing unit including an 8/16-bit microcontroller and limited memory for local data processing, a transceiver unit for wireless data communication (RF unit) and a power supply unit. According to the particular application, sensor nodes may also include additional optional components such as, a location finding system to discover their location, a mobilizer to change their position or configuration [39]. In the VSNs, it is desirable to keep the same low-power limits in the design of camera nodes, although in this case more energy will be needed for image data capture, processing and transmission. In this section, an overview of works that analyze energy consumption in visual sensor networks, as well as a short description on the current visual sensor node software and hardware architectures platforms are presented.

123

K. K. Hasan et al. Fig. 7 Hardware architectures for WSN node [51]

Fig. 8 Data-driven approaches classification to WSNs energy conservation [51]

4.1 Energy Conservation Approach for Data Driven WSN Having seen from all the previous works done, some critical points should be considered when using wireless sensor networks for real-time data transmission. These include restricted computational power, memory limitations, narrow bandwidth for communication and energy supplied present strong limits in sensor nodes. Therefore, energy saving is considered in many applications. The research work Barr and Asanovic showed that the main greedy factor of power consumption in the sensor node is the on-board radio transceiver [49]. Thus several studies were carried out to minimize the radio communication to achieve sufficient power savings. Methods for reducing energy consumption in WSNs are illustrated in Fig. 8 [50]. However, the most evident solution for reducing total power consumption by a sensor node is the data compression before transmitting process of data. Compression technology has a long history of development. This development continues with sensor networks, which provide a new design space with new design tradeoffs and metrics [43]. The lifetime of battery-operated camera nodes is limited by their energy consumption. Thus, if the size of data by compression could be minimized, it would reduce transmission power. The purpose of the image compression is to reduce the number of bits needed to represent an image. This is by removing as much as possible one or more of the three basic identified data redundancies namely, coding redundancy, inter-pixel redundancy and psychovisual redundancy [51–53]. Several data compression algorithms have been proposed in the literature. Even though those compression schemes are still under development, experimental results indicate that

123

Efficient Hardware-Based Image Compression Schemes

their compression rate and power reduction manners are absolutely stunning. There are possible methods to diminish resource restrict of wireless sensor nodes [54]. 4.2 Software Based Compression on VSN Platforms According to the computational power, the spectrum of VSN platforms limits from lightweight platforms (e.g., Mica2, Mica2Dot, MicaZ and Telos) through intermediate platforms (e.g., Yale XYZ) to the higher performance set PDA-class platforms (e.g., Intel Stargates) [82]. Table 1 introduces the typical quality of the three categories of VSN platforms. Lightweight platforms are only acceptable for simple sensing and detection tasks. However, they are highly resource-restricted in terms of both computational capabilities and the bandwidth that they can support. While the intermediate platforms, has more processing and memory resources than the lightweight platforms, PDA-class platforms are more powerful than the other platforms. However, they also consume more power [15,82]. The JPEG compression is implemented in most of the VSN platforms. For example, Cyclops [83], Panoptes [84], Meerkats [85], FireFly Mosaic [86], CITRIC [87] and Vision Mote [88] use JPEG or modified JPEG compression algorithms to perform intra-frame coding. No data compression or coding results are reported for either MeshEye [89] or MicrelEye [90] platforms. The only prototypes that investigate and report a scheme different than JPEG compression or its adaptations are XYZ-ALOHA and CITRIC. CITRIC which implements compressed sensing. XYZ-ALOHA uses AER (Address Event Representation), AER detects motion information of the visual scene. The software implemented compression techniques in VSN platforms is exposed in Table 2. However, most of the current Visual sensor platforms are software based, as seen in the well-known Cyclops sensor board [85] and CMUcam3 [92]. On the Cyclops camera node platform, the processing time of the 2D-DWT with 5/3 filter bank on an 8 bpp image of size (128×128) is around 8 s. In the same way, the processing time of the Loeffler 2-D 8-point DCT is around 7 s. For this reason, there is great demand for hardware based solutions [80,81]. In this survey paper, we present low power image compression and communication over wireless camera sensor networks (WCSNs) [80]. The proposed System-on-Chip is intended to be designed as a hardware coprocessor embedded in the camera sensor node. The proposed image compression chain includes a (block of) pixel interleaving scheme which significantly improves the robustness against packet loss in image communication. The authors discussed in depth the internal hardware architecture of the encoder chip which is planned to reach high performance running in FPGAs and in ASIC circuits.

Table 1 Comparison of WSN platforms [15]

Sensor type

Microcontroller type

Data rate

Mica2

ATmega128l (8-bit)

38.4 kbps

Mica2Dot

ATmega128l (8-bit)

38.4 kbps

MicaZ

ATmega128l (8-bit)

250 kbps

Tmote sky

MSP430F (16-bit)

250 kbps

I mote

ARM7 (32-bit)

723.2 kbps

XYZ

OKI ML67Q5002 (32-bit)

250 kbps

Stargate

Intel XScale (32-bit)

1–11 Mbps

CC2420

250 kbps

123

K. K. Hasan et al. Table 2 Compression techniques and targeted applications on VSN platforms [15] VSN platform

Compression techniques

Targeted applications

Cyclops [83]

JPEG compression

Object detection, Hand posture recognition

MeshEye [89]

–

Distributed intelligent surveillance

Panoptes [84]

Video surveillance

Meerkats [85]

Differential JPEG compression JPEG compression

FireFly Mosaic [86]

JPEG compression

Home activity clustering

Moving body tracking

MicrelEye [91]

–

Image classification, people detection

XYZALOHA [92] CITRIC [87]

Address event representation Compressive sensing

Vision Mesh [88]

JPEG compression

Pattern recognition (hand recognition, hand gesture recognition) Single target tracking, Camera localization using multi-target tracking Water conservancy engineering

The proposed compression scheme in [81], presented as a CMOS circuit, is intended to be embedded in the camera sensor. It will be considered as a co-processor for tasks related with image compression and data packetization, which unloads the main microcontroller so that it will spend less time in active mode. The interest of this solution is that the image compression chain includes a (block off) pixel interleaving scheme which significantly improves the robustness against packet loss in image communication. The main part of this paper focuses on the specification and the performance analysis of this solution when implemented in FPGA and ASIC circuits. 4.3 Hardware Based Compression on VSN Platforms Hardware implementations such as those using FPGAs are capable of accelerating these computations by exploiting the inherent algorithmic parallelism. DWT has been widely used for image and video coding, and many hardware implementations have been proposed in the research literature [77,98–102]. These implementations aim at reducing hardware complexity in order to improve system throughput. Among the traditional source coding approaches, the one suitable for WSNs is quality scalable coding. A coder is said to be quality most scalable if it generates a bitstream that can be decoded at multiple transmission bit rates. The wavelet based EZW, SPIHT and JPEG2000 are among the most popular quality-scalable image coders. JPEG2000 is not appropriate for WSNs because its transform (CDF9/7) and coding process (EBCOT) are both of high computational complexity and requires high memory [66]. Thus, from the hardware implementation viewpoints, SPIHT is favored over EBCOT coding [19,66,67]. Jinxing and Fuxia studied the growing performance of the SPIHT coding method compared with the other image coders. By increasing the size of the image block, the findings showed that the SPIHT coding method could better preserve the visual characteristics from the other coders [103]. While the DCT transform consumes the most power in the computation cost of the JPEG algorithm, many efforts to reduce its computational complexity have been recommended in the literature [93–97]. The authors in [105] use a parallel and pipelined (R–C) row–column decomposition method based on two processors for one dimensional DCT and an intermediate buffer. The proposed architecture allows the arithmetic units and main processing elements to run in parallel, which reduce both the internal storage and the computational complexity, and allows a high throughput [106].

123

Efficient Hardware-Based Image Compression Schemes

Another work comprised SPIHT as a compression tool is presented in [107]. The authors use a strip-based processing technique where the wavelet coefficients are buffered in a system of buffers that only store a small component of the wavelet transform coefficients. Initially, a small number of lines of image data are wavelet decomposed by DWT the lifting-based 5/3 DWT module. Subsequently, the wavelet coefficients are computed and after that buffered with a strip buffer. Lastly, the bitstream generated is transmitted without entropy coding.

5 Comparison and Survey Analysis Recently, transform-based methods are still very attractive and popular lossy image compression methods which are mainly based on DCT, such as JPEG and DWT, such as JPEG2000. DCT based methods algorithms are fast with low-complexity and low-memory. However, they often cause annoying blocking artifacts in the low bit rate [59–61]. The Discrete Wavelet Transform (DWT) has good localization according to time and frequency domains. Furthermore, because of their inherent multi-resolution nature, waveletbased coders are suitable for applications where scalability and tolerable degradation are important [64]. An appropriate wavelet is a wavelet that respects the power constraint of VSNs while providing an acceptable quality of the reconstructed image at the reception [74]. For this reason, low complexity wavelet-based coder (WBC) is becoming definitive in the design of VSNs. In addition, wavelet transformation can be carried out without the need for a full image transformation [65,71,73]. This enables very low memory implementation of the image coder. This is due to the fact that the primary concern in VSN is energy dissipation and memory usage, which are minimized with the lifting schemes [74]. The lifting scheme implementations of wavelets clearly outperform the traditional convolution-based wavelet in the context of VSNs. These lifting schemes minimize the number of operations as well as the memory occupation. It is a refined system of very short filters which are applied in a way that produces the same results as the convolution wavelet, achieving important computational and memory savings. It is then a suitable solution in this field for hardware implementation due to its simple and fast lifting based filtering method [74]. Many research works on the hardware implementation of lifting based DWT on FPGAs can be found [75–79]. Table 3 shows the results of performance, time and size utilization regarding some related works. Table 3 Performance, time and size utilization FOR SOME regarding DWT hardware related works Parameters

Architecture [75]

Architecture [76]

Architecture [77]

Architecture [78]

Architecture [79]

DWT Filter

5/3

5/3 or 9/7

9/7 or 5/3

9/7

5/3

Filter type

Lifting scheme

N/A

N/A

Lifting scheme

Lifting scheme

Image size

256 × 256

256 × 256

512 × 512

N/A

256 × 256

Input data precision Device

8 bits

8 bits

8 bits

8 bits

8 bits

XCV600E

XCV600E

XCV600E

APEX20KE

XC4VLX15

Computation time

2.36 ms

5.88 ms

N/A

N/A

N/A

Number slices

1,835

2,554

4,720

7,726

2,646

Frequency

108 MHz

45 MHz

75 MHz

66.8 MHz

117.6 MHz

123

K. K. Hasan et al. Table 4 Comparative analysis of the basic image compression algorithms techniques for visual sensor network implementation requirements [19] Block transform coding (BTC)

Subband and wavelet coding (SWC)

Subband and wavelet coding (SWC)

Subband and wavelet coding (SWC)

Discrete cosine transform

Embedded zerotree wavelet

Set-partitioning in hierarchical trees

Embedded block coding with optimized truncation

Lossy compression JPEG

Lossy compression

Lossy compression

Lossy compression JPEG2000

DCT

DWT

DWT

DWT

Entropy coding needed

Entropy coding needed

Entropy coding needed

Low memory

Modest memory

Entropy coding not needed Modest memory

Low compression ratio Cause annoying blocking artifacts in the low bit rate

Moderate compression ratio Simple, efficient but SPIHT has improved properties

Moderate compression Higher compression ratio ratio The nearest approach Not appropriate for hardware-based image WSNs, it requires heavy compression of WSNs computation

High memory

Set-partitioning in hierarchical trees (SPIHT) [56] is a fast, computationally simple but yet efficient image compression algorithm. It improves the performance of the embedded zerotrees wavelet (EZW) algorithm even without arithmetic coding [55,62]. Although the embedded block coding with optimized truncation (EBCOT) algorithm [57] gives higher compression efficiency as compared to SPIHT, it is the most computationally intensive part of JPEG2000 which comprises more than 50 % of computational complexity and requires additional memory allocation [57,66]. In the conventional SPIHT coding, a full wavelet-transformed image has to be stored and then the algorithm needs repeated access to all coefficient values [57]. This in turn, increases the cost of hardware image coders as a large memory bank is needed whereas the available memory space is limited due to power constraints such as in the implementation of on-board satellite image coders [68]. Different improvements have been proposed on the conventional SPIHT image compression algorithm. No lists coder SPIHT [69,70] is a list free particular form of SPIHT. It uses fixed size memory arrays instead of lists which are not influenced with the number of passes to be implemented. The low memory needed in the strip-based [71,72] and line based SPIHT image compression [65,73] is only related to the transformation level and the width of the image but not the height. These approaches are achieved by using a system of buffers that only store a small component of the wavelet transform coefficients. After the coding is finished, the buffer is released and is then ready for the next set of wavelet coefficients data lines. Thus as illustrated in Table 4, the modified low complexity coding and low memory setpartitioning in hierarchical trees (SPIHT) scheme based on the lifting implementation of wavelets has garnered significant improvements especially for implementation in hardware constrained environments such as in the context of WSNs.

6 Conclusions To be self-contained, we supply our paper with a short introduction on WSNs and VSNs paradigms and some related works. Our survey complements the aforementioned surveys as

123

Efficient Hardware-Based Image Compression Schemes

follows: In this paper, we offer a survey of image compression algorithms for visual sensor networks, such as the conventional state of the art standards such as JPEG and JPEG2000. We provide the advantages and shortcomings, as well as an open research issue for each compression standard/method. This paper has summarized a simple coding and low memory image compression scheme, i.e. lifting wavelet transform-based is presented with low memory and low complexity SPIHT without entropy coding. This approach reduces memory requirements significantly while retaining all the advantages of the embedded coding achieved through simple modifications to the SPIHT software using listless coder, strip-based and line based SPIHT. The performance degradation is minimal, in view of reduced memory and circuitry in a hardware environment. With the reduction in DWT coding complexity and the large reduction in memory requirement, this energy efficient image compression algorithm is consequently a nearer approach to be implemented in severely constrained hardware environments such as VSNs. The performance of the hardware based image compression scheme can be evaluated by using an image incorporated FPGA and in ASIC circuits. This would yield significant reductions in energy consumption and processing time.

References 1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422. 2. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. Communications Magazine, IEEE, 40(8), 102–114. 3. Culler, D., Estrin, D., & Srivastava, M. (2004). Guest editors’ introduction: Overview of sensor networks. Computer, 37(8), 41–49. 4. Tubaishat, M., & Madria, S. (2003). Sensor networks: An overview. Potentials, IEEE, 22(2), 20–23. 5. García-Hernández, C. F., Ibarguengoytia-Gonzalez, P. H., & Perez-Diaz, J. A. (2007). Wireless sensor networks and applications: A survey. IJCSNS International Journal of Computer Science and Network Security, 7(3), 264–273. 6. Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330. 7. Yang, Z., Liao, S., & Cheng, W. (2009). Joint power control and rate adaptation in wireless sensor networks. Ad Hoc Networks, 7(2), 401–410. 8. Charfi, Y., Wakamiya, N., & Murata, M. (2009). Challenging issues in visual sensor networks. Wireless Communications, IEEE, 16(2), 44–49. 9. Polastre, J., Szewczyk, R., Mainwaring, A., Culler, D., & Anderson, J. (2004). Analysis of wireless sensor networks for habitat monitoring. In Wireless sensor networks pp. (399–423). USA: Springer. 10. Magli, E., Mancin, M., & Merello, L. (2003, July). Low-complexity video compression for wireless sensor networks. In Multimedia and expo, 2003. ICME’03. Proceedings. 2003 International conference on (vol. 3, pp. 3–585). IEEE. 11. Soro, S., & Heinzelman, W. (2009). A survey of visual sensor networks. Advances in Multimedia. doi:10. 1155/2009/640386. 12. Akyildiz, I. F., Melodia, T., & Chowdhury, K. R. (2007). A survey on wireless multimedia sensor networks. Computer Networks, 51(4), 921–960. 13. Yaghmaee, M. H., & Adjeroh, D. A. (2009). Priority-based rate control for service differentiation and congestion control in wireless multimedia sensor networks. Computer Networks, 53(11), 1798–1811. 14. AlNuaimi, M., Sallabi, F., & Shuaib, K. (2011, April). A survey of wireless multimedia sensor networks challenges and solutions. In Innovations in information technology (IIT). 2011 international conference on (pp. 191–196). IEEE. 15. Tavli, B., Bicakci, K., Zilan, R., & Barcelo-Ordinas, J. M. (2012). A survey of visual sensor network platforms. Multimedia Tools and Applications, 60(3), 689–726. 16. Misra, S., Reisslein, M., & Xue, G. (2008). A survey of multimedia streaming in wireless sensor networks. Communications Surveys & Tutorials, IEEE, 10(4), 18–39. 17. Sharif, A., Potdar, V., & Chang, E. (2009). Wireless multimedia sensor network technology: A survey. In The 7th IEEE international conference on industrial informatics INDIN (pp. 606–613).

123

K. K. Hasan et al. 18. Sharif, A., Potdar, V., & Chang, E. (2009, June). Wireless multimedia sensor network technology: A survey. In Industrial informatics, 2009. INDIN 2009. 7th IEEE international conference on (pp. 606–613). IEEE. 19. Chew, L. W., Ang, L. M., & Seng, K. P. (2008, August). Survey of image compression algorithms in wireless sensor networks. In Information technology, 2008. ITSim 2008. International symposium on (vol. 4, pp. 1–9). IEEE. 20. Wang, Y. C. (2012). Data Compression Techniques in Wireless Sensor Networks. In M. L. Howard (Ed.), Pervasive Computing, New York: Nova Science Publishers, Inc. 21. Srisooksai, T., Keamarungsi, K., Lamsrichan, P., & Araki, K. (2012). Practical data compression in wireless sensor networks: A survey. Journal of Network and Computer Applications, 35(1), 37–59. 22. Huang, F., & Liang, Y. (2007, October). Towards energy optimization in environmental wireless sensor networks for lossless and reliable data gathering. In Mobile adhoc and sensor systems, 2007. MASS 2007. IEEE internatonal conference on (pp. 1–6). IEEE. 23. Liang, Y., & Peng, W. (2010). Minimizing energy consumptions in wireless sensor networks via twomodal transmission. ACM SIGCOMM Computer Communication Review, 40(1), 12–18. 24. Marcelloni, F., & Vecchio, M. (2010). Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization. Information Sciences, 180(10), 1924–1941. 25. Ferrigno, L., Marano, S., Paciello, V., & Pietrosanto, A. (2005, July). Balancing computational and transmission power consumption in wireless image sensor networks. In Virtual environments, human– computer interfaces and measurement systems, 2005. VECIMS 2005. Proceedings of the 2005 IEEE international conference on (6 pp.). IEEE. 26. Duran-Faundez, C., & Lecuire, V. (2008, April). Error resilient image communication with chaotic pixel interleaving for wireless camera sensors. In Proceedings of the workshop on real-world wireless sensor networks (pp. 21–25). ACM. 27. Wu, M., & Chen, C. W. (2003, October). Multiple bitstream image transmission over wireless sensor networks. In Sensors, 2003. Proceedings of IEEE (vol. 2, pp. 727–731). IEEE. 28. Wu, H., & Abouzeid, A. A. (2004, June). Power aware image transmission in energy constrained wireless networks. In Computers and communications, 2004. Proceedings. ISCC 2004. Ninth international symposium on (vol. 1, pp. 202–207). IEEE. 29. Mammeri, A., Khoumsi, A., Ziou, D., & Hadjou, B. (2008, August). Modeling and adapting JPEG to the energy requirements of VSN. In Computer communications and networks, 2008. ICCCN’08. Proceedings of 17th international conference on (pp. 1–6). IEEE. 30. Mammeri, A., Khoumsi, A., Ziou, D., & Hadjou, B. (2008). Energy-aware JPEG for visual sensor networks. The Maghrebian conference on software engineering and artificial intelligence MCSEAI, 2008, (pp. 1–7). 31. Mammeri, A., Khoumsi, A., Ziou, D., & Hadjou, B. (2008, October). Energy-efficient transmission scheme of JPEG images over Visual Sensor Networks. In Local computer networks, 2008. LCN 2008. 33rd IEEE conference on (pp. 639–647). IEEE. 32. Makkaoui, L., Lecuire, V., & Moureaux, J. M. (2010, July). Fast zonal DCT-based image compression for wireless camera sensor networks. In Image processing theory tools and applications (IPTA), 2010 2nd international conference on (pp. 126–129). IEEE. 33. Wagner, R., Nowak, R., & Baraniuk, R. (2003, September). Distributed image compression for sensor networks using correspondence analysis and super-resolution. In Image processing, 2003. ICIP 2003. Proceedings. 2003 international conference on (vol. 1, pp. 1–597). IEEE. 34. Boulgouris, N. V., & Strintzis, M. G. (2002). A family of wavelet-based stereo image coders. IEEE Transactions on Circuits and Systems for Video Technology, 12(10), 898–903. 35. Wu, H., & Abouzeid, A. A. (2005). Energy efficient distributed image compression in resourceconstrained multihop wireless networks. Computer Communications, 28(14), 1658–1668. 36. Wang, A., & Chandrakasan, A. (2001). Energy efficient system partitioning for distributed wireless sensor networks. In Acoustics, speech, and signal processing, 2001. Proceedings. (ICASSP’01). 2001 IEEE international conference on (vol. 2, pp. 905–908). IEEE. 37. Pradhan, S. S., Kusuma, J., & Ramchandran, K. (2002). Distributed compression in a dense microsensor network. Signal Processing Magazine, IEEE, 19(2), 51–60. 38. Lu, Q., Luo, W., Wang, J., & Chen, B. (2008). Low-complexity and energy efficient image compression scheme for wireless sensor networks. Computer Networks, 52(13), 2594–2603. 39. Nasri, M., Helali, A., Sghaier, H., & Maaref, H. (2010, March). Adaptive image transfer for wireless sensor networks (WSNs). In Design and technology of integrated systems in nanoscale era (DTIS), 2010 5th international conference on (pp. 1–7). IEEE.

123

Efficient Hardware-Based Image Compression Schemes 40. Nasri, M., Helali, A., Sghaier, H., & Maaref, H. (2010, October). Energy-efficient wavelet image compression in wireless sensor network. In Communication in wireless environments and ubiquitous systems: New challenges (ICWUS), 2010 international conference on (pp. 1–7). IEEE. 41. Nasri, M., Helali, A., Sghaier, H., & Maaref, H. (2011). Adaptive image compression technique for wireless sensor networks. Computers & Electrical Engineering, 37(5), 798–810. 42. Wu, H., & Abouzeid, A. A. (2004, August). Energy efficient distributed JPEG2000 image compression in multihop wireless networks. In Proceedings of IEEE workshop on applications and services in wireless networks (pp. 152–160). 43. Huu, P. N., Tran-Quang, V., & Miyoshi, T. (2010, July). Image compression algorithm considering energy balance on wireless sensor networks. In 2010 8th IEEE international conference on industrial informatics (INDIN) (pp. 1005–1010). Osaka, Japan: IEEE. 44. Dong, H., Lu, J., & Sun, Y. (2006). A distributed wavelet-based image coding for wireless sensor networks. In Intelligent Control and Automation, Lecture Notes in Control and Information Sciences (vol. 344, pp. 72–82). Berlin, Heidelberg: Springer. 45. Jamali, M., Zokaei, S., & Rabiee, H. R. (2010, June). A new approach for distributed image coding in wireless sensor networks. In Computers and communications (ISCC), 2010 IEEE symposium on (pp. 563–566). IEEE. 46. Devaguptapu, D., & Krishnamachari, B. (2003, April). Applications of localized image processing techniques in wireless sensor networks. In Proceedings of SPIE (vol. 5090, pp. 247–256). 47. Ganesan, D., Greenstein, B., Perelyubskiy, D., Estrin, D., & Heidemann, J. (2003, November). An evaluation of multi-resolution storage for sensor networks. In Proceedings of the 1st international conference on embedded networked sensor systems (pp. 89–102). ACM. 48. Servetto, S. D. (2003, September). Sensing Lena-Massively distributed compression of sensor images. In Image processing, 2003. ICIP 2003. Proceedings. 2003 international conference on (vol. 1, pp. I–613). IEEE. 49. Barr, K. C., & Asanovi´c, K. (2006). Energy-aware lossless data compression. ACM Transactions on Computer Systems (TOCS), 24(3), 250–291. 50. Anastasi, G., Conti, M., Di Francesco, M., & Passarella, A. (2009). Energy conservation in wireless sensor networks: A survey. Ad Hoc Networks, 7(3), 537–568. 51. Subramanya, A. (2001). Image compression technique. Potentials, IEEE, 20(1), 19–23. 52. Yang, M., & Bourbakis, N. (2005, August). An overview of lossless digital image compression techniques. In Circuits and systems, 2005. 48th Midwest symposium on (pp. 1099–1102). IEEE. 53. Jain, A. K. (1981). Image data compression: A review. Proceedings of the IEEE, 69(3), 349–389. 54. Taubman, D., & Marcellin, M. (2001). JPEG2000: Image compression fundamentals, standards and practice. Boston: Kluwer Academic Publishers. 55. Shapiro, J. M. (1993). Embedded image coding using zerotrees of wavelet coefficients. IEEE Transactions on Signal Processing, 41(12), 3445–3462. 56. Said, A., & Pearlman, W. A. (1996). A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and systems for video technology, 6(3), 243–250. 57. Taubman, D. (2000). High performance scalable image compression with EBCOT. IEEE Transactions on Image Processing, 9(7), 1158–1170. 58. Tseng, H. W., & Chang, C. (2005). A very low bit rate image compressor using transformed classified vector quantization. Informatica-LJUBLJANA-, 29(3), 335. 59. Loeffler, C., Ligtenberg, A., & Moschytz, G. S. (1989, May). Practical fast 1-D DCT algorithms with 11 multiplications. In Acoustics, speech, and signal processing, 1989. ICASSP-89, 1989 international conference on (pp. 988–991). IEEE. 60. Feig, E., & Winograd, S. (1992). Fast algorithms for the discrete cosine transform. IEEE Transactions on Signal Processing, 40(9), 2174–2193. 61. Jeong, H., Kim, J., & Cho, W. K. (2004). Low-power multiplierless DCT architecture using image correlation. IEEE Transactions on Consumer Electronics, 50(1), 262–267. 62. Said, A., & Pearlman, W. A. (1993, May). Image compression using the spatial-orientation tree. In Circuits and systems, 1993, ISCAS’93, 1993 IEEE international symposium on (pp. 279–282). IEEE. 63. Zhang, H., & Fritts, J. (2004, January). EBCOT coprocessing architecture for JPEG 2000. In Proceedings of SPIE (vol. 5308, pp. 1333–1340). 64. Mallat, S. G. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674–693. 65. Chrysafis, C., & Ortega, A. (2000). Line-based, reduced memory, wavelet image compression. IEEE Transactions on Image Processing, 9(3), 378–389.

123

K. K. Hasan et al. 66. Lian, C. J., Chen, K. F., Chen, H. H., & Chen, L. G. (2003). Analysis and architecture design of block-coding engine for EBCOT in JPEG 2000. IEEE Transactions on Circuits and Systems for Video Technology, 13(3), 219–230. 67. Lee, D. G., & Dey, S. (2002). Adaptive and energy efficient wavelet image compression for mobile multimedia data services. In Communications, 2002. ICC 2002. IEEE international conference on (vol. 4, pp. 2484–2490). IEEE. 68. Parisot, C., Antonini, M., Barlaud, M., Lambert-Nebout, C., Latry, C., & Moury, G. (2000). On board strip-based wavelet image coding for future space remote sensing missions. In Geoscience and remote sensing symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 international (vol. 6, pp. 2651–2653). IEEE. 69. Wheeler, F. W., & Pearlman, W. A. (2000). SPIHT image compression without lists. In Acoustics, speech, and signal processing, 2000. ICASSP’00. Proceedings. 2000 IEEE international conference on (vol. 6, pp. 2047–2050). IEEE. 70. Lin, W. K., & Burgess, N. (1998, November). Listless zerotree coding for color images. In Signals, systems and computers, 1998. Conference record of the thirty-second asilomar conference on (vol. 1, pp. 231–235). IEEE. 71. Bhattar, R. K., Ramakrishnan, K. R., & Dasgupta, K. S. (2002). Strip based coding for large images using wavelets. Signal Processing: Image Communication, 17(6), 441–456. 72. Li Wern, C., Wai Chong, C., Li-minn, A., & Kah Phooi, S. (2009). Very low-memory wavelet compression architecture using strip-based processing for implementation in wireless sensor networks. EURASIP Journal on Embedded Systems, doi:10.1155/2009/479281. 73. Chang, W. H., Lee, Y. S., Peng, W. S., & Lee, C. Y. (2001, May). A line-based, memory efficient and programmable architecture for 2D DWT using lifting scheme. In Circuits and systems, 2001. ISCAS 2001. The 2001 IEEE international symposium on (vol. 4, pp. 330–333). IEEE. 74. Hadjou, B., Mammeri, A., & Khoumsi, A. (2011, April). Determining suitable wavelet filters for visual sensor networks. In Electronics, communications and photonics conference (SIECPC), 2011 Saudi international (pp. 1–5). IEEE. 75. Dia, D., Zeghid, M., Saidani, T., Atri, M., Bouallegue, B., Machhout, M., & Tourki, R. (2009). Multilevel discrete wavelet transform architecture design. In Proceedings of the world congress on engineering (vol. 1). 76. Palero, R. J. C., Gironés, R. G., & Cortes, A. S. (2006). A novel FPGA architecture of a 2-D wavelet transform. The Journal of VLSI Signal Processing, 42(3), 273–284. 77. Benkrid, A., Benkrid, K., & Crookes, D. (2003, April). Design and implementation of a generic 2D orthogonal discrete wavelet transform on FPGA. In Field-programmable custom computing machines, 2003. FCCM 2003. 11th annual IEEE symposium on (pp. 162–172). IEEE. 78. Acharya, T., & Chakrabarti, C. (2006). A survey on lifting-based discrete wavelet transform architectures. The Journal of VLSI Signal Processing, 42(3), 321–339. 79. Angelopoulou, M. E., Masselos, K., Cheung, P. Y., & Andreopoulos, Y. (2008). Implementation and comparison of the 5/3 lifting 2D discrete wavelet transform computation schedules on FPGAs. Journal of Signal Processing Systems, 51(1), 3–21. 80. Kaddachi, M. L., Soudani, A., Lecuire, V., Torki, K., Makkaoui, L., & Moureaux, J. M. (2012). Low power hardware-based image compression solution for wireless camera sensor networks. Computer Standards & Interfaces, 34(1), 14–23. 81. Kaddachi, M. L., Soudani, A., Nouira, I., Lecuire, V., & Torki, K. (2010, December). Efficient hardware solution for low power and adaptive image-compression in WSN. In Electronics, circuits, and systems (ICECS), 2010 17th IEEE international conference on (pp. 583–586). IEEE. 82. Hill, J., Horton, M., Kling, R., & Krishnamurthy, L. (2004). The platforms enabling wireless sensor networks. Communications of the ACM, 47(6), 41–46. 83. Rahimi, M., Baer, R., Iroezi, O. I., Garcia, J. C., Warrior, J., Estrin, D., & Srivastava, M. (2005, November). Cyclops: In situ image sensing and interpretation in wireless sensor networks. In Proceedings of the 3rd international conference on embedded networked sensor systems (pp. 192–204). ACM. 84. Feng, W. C., Kaiser, E., Feng, W. C., & Baillif, M. L. (2005). Panoptes: Scalable low-power video sensor networking technologies. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP), 1(2), 151–167. 85. Boice, J., Lu, X., Margi, C., Stanek, G., Zhang, G., Manduchi, R., & Obraczka, K. (2006, October). Meerkats: A power-aware, self-managing wireless camera network for wide area monitoring. In Distributed smart cameras workshop-SenSys06. 86. Rowe, A., Goel, D., & Rajkumar, R. (2007, December). Firefly mosaic: A vision-enabled wireless sensor networking system. In Real-time systems symposium, 2007. RTSS 2007. 28th IEEE international (pp. 459–468). IEEE.

123

Efficient Hardware-Based Image Compression Schemes 87. Chen, P., Ahammad, P., Boyer, C., Huang, S. I., Lin, L., Lobaton, E., et al. (2008, September). CITRIC: A low-bandwidth wireless camera network platform. In Distributed smart cameras, 2008. ICDSC 2008. Second ACM/IEEE international conference on (pp. 1–10). IEEE. 88. Zhang, M., & Cai, W. (2010, July). Vision mesh: A novel video sensor networks platform for water conservancy engineering. In Computer science and information technology (ICCSIT), 2010 3rd IEEE international conference on (vol. 4, pp. 106–109). IEEE. 89. Hengstler, S., Prashanth, D., Fong, S., & Aghajan, H. (2007, April). MeshEye: A hybrid-resolution smart camera mote for applications in distributed intelligent surveillance. In Information processing in sensor networks, 2007. IPSN 2007. 6th international symposium on (pp. 360–369). IEEE. 90. Kerhet, A., Magno, M., Leonardi, F., Boni, A., & Benini, L. (2007). A low-power wireless video sensor node for distributed object detection. Journal of Real-Time Image Processing, 2(4), 331–342. 91. Teixeira, T., Culurciello, E., Park, J. H., Lymberopoulos, D., Barton-Sweeney, A., & Savvides, A. (2006, April). Address-event imagers for sensor networks: Evaluation and modeling. In Proceedings of the 5th international conference on Information processing in sensor networks (pp. 458–466). ACM. 92. Rowe, A., Goode, A., Goel, D., & Nourbakhsh, I. (2007, May). CMUcam3: An open programmable embedded vision sensor. In International conferences on intelligent robots and systems. 93. Osman, H., Mahjoup, W., Nabih, A., & Aly, G. M. (2007, November). JPEG encoder for low-cost FPGAs. In Computer engineering and systems, 2007. ICCES’07. International conference on (pp. 406– 411). IEEE. 94. Agostini, L. V., Silva, I. S., & Bampi, S. (2007). Multiplierless and fully pipelined JPEG compression soft IP targeting FPGAs. Microprocessors and Microsystems, 31(8), 487–497. 95. Mallireddy, S. R., & Commuri, S. (2010, April). Run time compression of image data in wireless sensor networks. In Networking, sensing and control (ICNSC), 2010 international conference on (pp. 512–517). IEEE. 96. Papadonikolakis, M., Pantazis, V., & Kakarountas, A. P. (2007, April). Efficient high-performance ASIC implementation of JPEG-LS encoder. In Design, automation and test in Europe conference and exhibition, 2007. DATE’07 (pp. 1–6). IEEE. 97. Zhou, R., Liu, L., Yin, S., Luo, A., Chen, X., & Wei, S. (2010, May). A VLSI design of sensor node for wireless image sensor network. In Circuits and systems (ISCAS), proceedings of 2010 IEEE international symposium on (pp. 149–152). IEEE. 98. Alam, M., Rahman, C. A., Badawy, W., & Jullien, G. (2003, June). Efficient distributed arithmetic based dwt architecture for multimedia applications. In System-on-chip for real-time applications, 2003. Proceedings. The 3rd IEEE international workshop on (pp. 333–336). IEEE. 99. Ritter, J., & Molitor, P. (2001, February). A pipelined architecture for partitioned DWT based lossy image compression using FPGA’s. In Proceedings of the 2001 ACM/SIGDA ninth international symposium on field programmable gate arrays (pp. 201–206). ACM. 100. Kotteri, K. A., Barua, S., Bell, A. E., & Carletta, J. E. (2005). A comparison of hardware implementations of the biorthogonal 9/7 DWT: Convolution versus lifting. IEEE Transactions on Circuits and Systems II: Express Briefs, 52(5), 256–260. 101. Martina, M., & Masera, G. (2005, September). Low-complexity, efficient 9/7 wavelet filters implementation. In Image processing, 2005. ICIP 2005. IEEE international conference on (vol. 3, pp. III–1000). IEEE. 102. Villasenor, J. D., Belzer, B., & Liao, J. (1995). Wavelet filter evaluation for image compression. IEEE Transactions on Image Processing, 4(8), 1053–1060. 103. Wang, J., & Zhang, F. (2010, June). Study of the Image Compression based on SPIHT Algorithm. In Intelligent computing and cognitive informatics (ICICCI), 2010 international conference on (pp. 130–133). IEEE. 104. Jizheng, X., W. Feng, et al. (2008). Directional lapped transforms for image coding. In Data compression conference, 2008. DCC 2008. 105. Winkler, S. (2005). Digital video quality: Vision models and metrics. New York: Wiley. 106. Ruiz, G. A., Michell, J. A., & Burón, A. (2006). High throughput parallel-pipeline 2-D DCT/IDCT processor chip. Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, 45(3), 161–175. 107. Chew, L. W., Chia, W. C., Ang, L. M., & Seng, K. P. (2009). Very lowmemory wavelet compression architecture using strip-based processing for implementation in wireless sensor networks. Eurasip Journal on Embedded Systems, 2009, 479281.

123

K. K. Hasan et al. Khamees Khalaf Hasan received the B.Sc. and M.Sc. degrees in electrical engineering and in Communication Engineering from University Of Technology (U.O.T) Baghdad, Iraq in 1985 and 2005, respectively, where he is currently working toward the Ph. D. degree in the School of Electrical and Electronic Engineering, Universiti Sains Malaysia (USM). His major research interests include video and image coding in wireless sensor networks.

Umi Kalthum Ngah (B.Sc. (Hons) Sheffield, M.Sc. (USM), Ph.D (USM)) was born in Penang, Malaysia on the 11th of February 1959. She received her B.Sc. (Hons.) in Computer Science from the University of Sheffield in 1981. In 1995, she received her M.Sc. in Electronic Engineering (majoring in Image Processing and Knowledge Based Systems) from Universiti Sains Malaysia (USM) and then pursued further degree at the same university where she received her Ph. D. in the same area in the year 2007. She has been with USM since the year 1981, starting her career as a tutor. At the present moment, she is attached to the School of Electrical and Electronic Engineering, USM Engineering Campus. Her current research interests include image processing particularly medical imaging, knowledge based and artificial intelligence systems and biomedical engineering focusing on intelligent diagnostic systems. Her work has been published in numerous international and national journals, chapters in books, international and national proceedings.

Mohd Fadzli Mohd Salleh was born in Bagan Serai, Perak, Malaysia. He received his B.Sc. degree in Electrical Engineering from Polytechnic University, Brooklyn, New York, U.S., in 1995. He was then a Software Engineer at MOTOROLA Penang, Malaysia, in R&D Department until July 2001. He obtained his M.Sc. degree in Communication Engineering from UMIST, Manchester, U.K., in 2002. He completed his Ph. D. degree in image and video coding for mobile applications, in June 2006 from the Institute for Communications and Signal Processing (ICSP), University of Strathclyde, Glasgow, U.K. Currently, he is a senior lecturer in the School of Electrical and Electronic Engineering, Universiti Sains Malaysia.

123