Hyperspectral image compression approaches: opportunities, challenges and future directions discussion RUI DUSSELAAR1, MANORANJAN PAUL2 1School 2School
of Computing and Mathematics, Charles Sturt University, Bathurst, NSW, Australia.2795 of Computing and Mathematics, Charles Sturt University, Bathurst, NSW, Australia.2795 * Corresponding author:
[email protected]; mpaul@ csu.edu.au
Received XX Month XXXX; revised XX Month, XXXX; accepted XX Month XXXX; posted XX Month XXXX (Doc. ID XXXXX); published XX Month XXXX
This paper establishes the review of the recent study in the field of hyperspectral (HS) image compression approaches. Lately, image compression techniques have achieved significant advances from diverse types of coding standards/approaches. HS image compression requires an unconventional coding technique because of its unique, multiple dimensional structures. The data redundancy exists in both inter-band and in intra-band. The survey summarises current literature in inter- and intra-band compression methods. The challenges, opportunities and future research possibilities regarding HS image compression are further discussed. The experimental results are also provided for validity and applicability of the existing HS image compression techniques. OCIS codes: (100.0100 ) Image processing, (100.6890) Three-dimensional image processing; (100.2000) Digital image processing; (280.0280) Remote sensing and sensors;
Introduction Multispectral remote captures image data in a few relatively broad wavelength bands, typically 3 to 15 of bands. By comparison, HS remote sensors collect image data simultaneously in dozens or hundreds of narrow, adjacent spectral bands that cover a wide range of wavelengths extending from 400 to 2500 nanometre (nm) [1]. A normal visible light spectrum for human-vision is about 400 nm to 700 nm in wavelength. HS images contain an extensive range of spectral information this can provide observation power beyond the capability of human vision. However, this benefit also comes at a price, which is a wealthy spectrum of information also contains a huge amount of data. The problem is how to process enormous HS images while preserving the required useful information. Of course this is a fundamental problem for compression techniques. As we know data compression can be performed because there is unwanted redundancy in the data. High data redundancy may obtain a high compression ratio. On the contrary, lower data redundancy may get a low compression ratio. In the case of an HS image, it typically contains large amounts of image data redundancy. There are two types of redundancy that generally exists in an HS image, (i) Spatial redundancy: It is a redundancy corresponding to statistical dependencies among pixels; (ii) Spectral redundancy: It is a redundancy corresponding to pixels with the same spatial location in adjacent spectral bands. Spectral redundancy is a particular feature of HS image. In the past two decades research efforts have been directed at the reduction of spatial redundancy. Several compression standards have matured and formed
worldwide image compression standards like JPEG[2] or JPEG2000[3]. Nowadays, compression techniques aimed at eliminating spectral redundancy are still being researched. A desired HS image compression technique must be based on eliminating spatial redundancy as well as taking full consideration of spectral redundancy. The purpose is to reduce the spatial and spectral correlation consecutively and therefore achieve an optimised compression result.
Methodology 1.
Lossless and lossy compression methods Lossless and lossy compression methods, depending on whether the original image can be precisely re-generated from the compression data [4]. A tree structure of different HS image compression strategies is summarised in Fig 1. Lossless compression has no signal loss. The decoder is able to recover the compressed data back to the original data. Lossy compression method selectively discards part of signals. Decoder usually cannot recover data exactly but it can lead to relatively higher compression ratios. Lossless compression is used for applications that require the reconstructed image to restore to the original signal with high precision. Because of the intrinsic entropy of the data, lossless compression algorithm generally achieves modest compression ratio and cost more storage space for no loss of image data. By contrast, a lossy compression algorithm of HS image technique is for preserving essential spectral information of target objects, which attempt to balance of compression
Fig.1. Block diagram of general categorisation of compression methods efficiency and loss of information. Lossy image compression mainly uses predictive coding methods, transform coding etc. to compress data reducing correlations of pixels. Lossy compression is used when the user can tolerate some signal loss. Such as medical images, materials analysis commonly requires lossless compression; general web applications often use lossy compression. We’ll have briefly looked at the major types of compression algorithms in this subsection. Further introduction and discussion about the technical characteristics of typical compression methods are provided in subsequent paragraphs. The lossless compression methods are generally grouped into dictionary-based schemes and statistical schemes [5]. However dictionary-based schemes can be used for lossy compression [6], by means of quantization or entropy coding. Dictionary-based schemes do not require a prior knowledge of the source symbol probability distribution; they encode variable-length strings of symbols as single tokens, each token retrieve for a phrase dictionary such as Lempel-ZivWelch (LZW) compression algorithm [7]. Statistical-based schemes require distribution knowledge where the compression takes place based on the frequency of input characters. The most well-known statistic-based algorithms are Huffman Coding [25] and Arithmetic Coding [26, 27]. Moreover, another type of compression technique used Lookup Tables (LUT) [28, 29]. The LUT searches the previous band for a pixel equal to the current band in the same position, called a predictor. The predictor is used as a key to search Lookup tables to speed up the search process.
several groups of bands (GOBs). The authors apply intra-band prediction to the first band in each GOB. The hypothesis is that two blocks (8x8 pixels) located in the same position of adjacent HS bands are highly similar. Likewise, the authors of [10] observe that in HS images exists strong similarities between adjacent bands. They use the first band for intra-band prediction encoded and the remaining bands are inter-band encoded using fractal encoded. The encoding process of fractal coding is to use the similar interpolation method. An original image is divided into sub-images and then in the fractal set to find a similar sub-image. Decoding is completed by an iteration procedure. The encoding time is long because of the search used to find the self-similarities. Conversely, decoding time is relatively shorter. The image becomes resolution independent after being converted to fractal code. In summary of BS algorithm, selecting a more efficient GOBs measure is still vital to performance. There is no universal metric of GOBs that is applicable to all HS images in different wavelengths. Moreover, in some experimental results [9, 10] show that the algorithms can only achieve effective compression at a low bit rate.
Channel
2.
Inter-band / Intra-band compression methods Currently, available literature presents various methods regarding HS image compression studies. As mentioned previously, HS image is a three-dimensional data structure, capturing both spatial and spectral information. In general, two key steps are included into HS image compression processes. The first step of HS image compression is normally spectral decorrelation. We call it inter-band compression. This step is working on dimensional reduction. It is a very important stage to eliminate spectral redundancy. The second stage, mainly focuses on various types of compression encoders. It can be 2D or 3D compression encoder. That is considered as the intra-band compression method. Both inter-band compression and intra-band compression actually should be considered. In some cases, those two procedures can be combined as one step. A general HS image compression procedure is demonstrated at Fig 2. Band-to-band correlation is usually very high in HS image, removing such redundant information can achieve a significant compression ratio [8]. Band selection-based (BS) method is to select a subset of bands from HS image. Zhao, et al.[9] introduce an algorithm based on intra-band prediction and inter-band fractal encoding. HS bands are partitioned into
Fig.2. Spectral/spatial HS image compression procedure 3.
Compression methods based on different coding algorithms We have given a preliminary categorization of HS compression methods. Next, based on the specific different compression methods, we’ll have a review. (A) PCA (Principle Component Analysis) transform based inter-band compression PCA compression is probably the most popular and commonly used in HS compression. PCA is developed based on the principle of the Karhunen-Loêve transform (KLT). KLT converts M dimensional data to low-order uncorrelated components. The largest variance is
concentrated in the first N components (M0, Ѱ(x,y,z)= 4P(x,y-1,z); an estimation of local differences 𝑑̂ z(t) can be the inner product of a weight vector 𝑑̂ z(t)=W (t)Uz(t) where the difference between the local sum and the corresponding scaled original pixels is tracked and stored in a vector Uz(t) named, local difference vector. Then in the encoding stage, to encode the prediction residuals, the sample-adaptive entropy coding approach used. A predictor followed by an encoder of the prediction residuals is introduced. For an end user to use the CCSDS-123 standard they need to select a suitable combination of parameters for a specific application scenario. We have listed a summary of HS compression literature based on the above discussion in Table 1.
Experiments To observe the validity and suitability of different image compression approaches, four completely different datasets were used for experiments. We used Peak Signal to Noise Ratio (PSNR) to compare restoration results. The multiple experiments were performed by the relevant Benchmark encoders such as JPEG [2, 12], PCA-DCT [14], HEVC [62, 63] and other wavelet-based encoders such as JPEG2000 [16], 3DSPIHT [40], ASWDR [74]and EZW[40, 41]. They all are very competitive encoders for HS compression. At present, many new developed methods are actually still based on these algorithms with partial improvements. In order to compare with the state-of-the-art schemes, we also included AT3DSPECK [76], AT-3DSPIHT [46], and F. Zhao's [60] algorithms. They all standouts in term of PSNR for HS compression. Dataset Publicly available HS datasets can be a good source for HS image research and result verification. Specifically, Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) reflectance data is a benchmark imaging system. AVIRIS data is mainly collected for identifying, measuring, and monitoring of the earth's surface and atmosphere based on molecular absorption and particle scattering signatures.
Table 1. Summary of major contributions and challenges of existing HS image compression approaches. Algorithms Authors Contribution Inter-band compression KLT/PCA Transform
Du and Fowler 2007, Wang, Wu et al. 2009,
Pairwise orthogonal transform (POT),
Nian, Liu et al. 2016
Band-selection-based inter-bands compression
Wang, Gong et al. 2015, Zhu, Zhao et al. 2015, Zhao, Zhu et al. 2016
Tensor
Zhang, Zhang et al. 2015, Veganzones, Cohen et al. 2016)
Vector quantization transform
Zhang, Li et al. 2012, Zhao and Jing 2013, Li, Ren et al. 2014
Discrete cosine transform/3DCT
Rasti, Sveinsson et al. 2012, Qiao, Ren et al. 2014)
3D Wavelets Transforms
Tang and Pearlman 2006, Shingate, Sontakke et al. 2010, Zala and Parmar 2013, George and Manimekalai 2014 (Lin and Hwang 2010, Castrodad, Xing et al. 2011, Lin and Hwang 2011, Ülkü and Töreyin 2015)
PCA-based compression techniques discard part of the image signal. This may not be suitable for specific applications. Also, one of the major difficulties to apply PCA is to determine the optimal number of PCs for implementation. It overcomes the problem of KLT, such as bit depth expansion, lack of scalability and reduces memory requirements Selecting efficient GOBs measure is still vital to choose. There is no universal metric of GOBs applicable to all HS images in different wavelengths. Moreover, some of the experimental results [15, 16] show that the algorithms can only achieve effective compression at low bitrate. Tensor can well keep the spatial structure of data. But a huge amount of spectral information contained in an HS image to generate a tensor by using a multiplicative update algorithm makes computation extremely complicated and sometimes even unfeasible. To reduce the computational cost and speed up the convergence is very important for using tensor.
Intra-band compression
Dictionary learning and prediction based compression
Video Coding H.264/HEVC
Gao, Ji et al. 2014, Dusselaar, Paul et al. 2015, Podder, Paul et al. 2015
VQ can achieve a compression result by reducing colour distribution. However, the design of a codebook is considered to be a challenging problem. One of the traditional compression techniques. It can well remove the intra-band correlation but inter-band correlation is left untreated. Therefore, 3D-DCT approach is introduced. It’s an enhancement of DCT to extend the multiple dimensional process level. However, the reconstructed image gets blocking effects which severely degrades the visual quality. 3D transform is not specifically designed for features of HS image. It doesn’t fully utilise the common background of inner bands and spectral redundancy. Dictionary-based learning methods take consideration of inherent characteristics of the input data. Using only a few numbers of dictionary elements to present the dataset. It’s a data-driving method. Using a dictionary generating process is a key step to determine the compression performance. HEVC standard has shown significant improvement over state of the art transformation based still-image coding standard with spectral prediction modelling.
HS library
Provider
Table 2 publicly available HS datasets Spectral Range
AVIRIS Airborne Visible Infrared Imaging spectrometer HYDICE The Hyperspectral Digital Imagery Collection Experiment Natural scenes dataset
NASA
0.4~2.5µm
Naval Research Laboratory
0.4~2.5µm
University of Manchester Foster, D.H. etc.
0.4~0.7µm
Purdue’s Indianan Indian Pine test site
The Purdue University Research Repository
0.4~2.5µm
Scene data
SCIEN, Stanford University
0.4~2.5µm
USGS Digital Spectral Library
USGS Spectroscopy Lab
0.4~2.5µm
Description
AVIRIS is the most widely used HS database. AVIRIS data is basically related to the global environment and climate change. http://aviris.jpl.nasa.gov/ HYDICE is a 206 channel imaging spectrometer. The data was collected on ERIM CV 580 aircraft and a C-141 aircraft at altitudes up to 40,000 feet [76]. The natural scenes dataset consist of a mixture of rural scenes from the Minho region of Portugal, containing rocks, trees, leaves and grass, and urban scenes from the cities of Porto and Braga. http://personalpages.manchester.ac.uk/staff/david.foster /Hyperspectral_images_of_natural_scenes_04.html Purdue’s Indianan Indian Pine test site is another most widely used HS image. It provides 12 band moderate dimension Images and 220 band HS images. https://engineering.purdue.edu/~biehl/MultiSpec/hyper spectral.html The dataset contains images of faces, landscapes and buildings. The images have been recorded with two HySpex line-scan imaging spectrometers covering the spectral ranges 0.4 to 1 micrometres and 1 to 2.5 micrometres. https://scien.stanford.edu/index.php/hyperspectralimage-data/ USGS is a very useful laboratory data library provided various remote sensing spectrometers. https://speclab.cr.usgs.gov/spectral-lib.html
Table 3. Rate distortion performance of four HS images using benchmark methods. Low Bit rates 0.5 bpppb Images (35 PSNR or more) F.zhao’s[60] Cuprite_1 Jasper Ridge Lunar Lake
52.80 52.76 52.22
Cuprite_1 Jasper Ridge Lunar Lake
55.49 56.34 54.71
AT3DSPIHT[46] 51.40 51.16 51.74
AT3DSPECK[77] 52.07 51.11 52.01
EZW
ASWDR
3D-SPIHT
39.39 36.47 33.56
39.51 38.65 29.87
39.57 39.85 31.24
47.24 45.56 44.74
48.44 48.57 49.69
High bit rate 1 bpppb 54.90 56.08 54.27
54.32 55.93 55.01
44.35 43.27 43.45
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
Fig.4 Rate distortion performance of nine HS images using the JPEG2000, JPEG, PCA-DCT, and HEVC encoder techniques.
They deliver calibrated images with 224 contiguous spectral bands and approximately 10 nm spectral resolutions covering the 0.4 – 2.5-μm. For more details of HS datasets can refer to Table 2. HS images for our experiments were captured using a Brimrose HS camera at Griffith University, Australia. The resolution of the images is 1392×1204×61 i.e. 61 wavelength bands, starting from 400nm to 1000nm wavelength bands, each with approximately 10nm interval steps. On top of the dataset collected from the Brimrose HS camera, we also used natural scenes [75], AVIRIS and Indian Pine dataset. Performance evaluation used four HS datasets covering both airborne and groundbased types of HS images. The PSNR gain is drawn based on the performance of the coding standard. The PSNR is defined as: PSNR=10 × log10 (
𝑃𝑒𝑎𝑘𝑖2 √𝑀𝑆𝐸
)
(1)
Where, 𝑝𝑒𝑎𝑘𝑖 is the maximum possible pixel value of the HS image, MSE is the mean square error. In Fig.4, Flower, Vase, Scene5 and Scene6 represent HS laboratory data, which are the ground-based types HS images. Those images have a relatively shorter wavelength in comparison with the AVIRIS dataset. It is clearly seen from Fig. 4 that in Flower, Vase, Scene 5, Indian-pines HS images, JPEG2000 show less rate-distortion in the same level of bit per pixel per band (bpppb). HEVC encoder performs solidly throughout for Cuprites datasets. HEVC achieved the best result in Scene 6. To summarise, JPEG2000 encoder has more obvious advantages in the ground-based HS images. Its overall performance maintains at relatively high levels. HEVC also have a superior performance, particularly in the airborne type of HS images. PCA-DCT reconstructs HS image to a lower dimensional image, uses an orthogonal transformation. It normally uses only first few PCs and discards the rest of the components for compression. PCA-DCT-based approach selects fewer principal components and changed the physical structure of the HS image. As a consequence, PCA-DCT could not obtain higher PSNR. But PCA is still able to obtain reasonably good image quality by judging of human vision system. It has been widely used for utilities of HS image lossy compression for a number of application areas. The recently high performance methods: AT-3DSPECK [76], AT3DSPIHT [46], F. Zhao's [60] algorithm, as well as popular wavelet-based encoders 3D-SPIHT, ASWDR and EZW have been evaluated in Table 3. We focused on PSNR evaluations of recently introduced compression methods. The chosen encoders: adaptively scanned wavelet difference reduction (ASWDR), EZW and 3D-SPIHT, ASWDR is one of the most recent image compression algorithms [74]. The rate-distortion results are summarised in two categories: low bit rates at 0.5bpppb and high bit rates at 1 bpppb. F. Zhao's algorithm outperforms than other methods. Both AT-3DSPECK and AT-3DSPIHT can provide competitive results. EZW, ASWDR couldn’t get the superior performance mainly because the 2-D tree structure which couldn’t eliminate inter-band redundancy very well.
Conclusion and future research direction In this review paper, we extensively reviewed current HS image compression approaches including current standard. Because of unique features of HS images, both intra-band and inter-band compression need to be considered based on the application specific demands. The applicability of the encoder for the purpose of HS image compression is validated by our experimental results. Overall, both HEVC and JPEG2000 achieves high performance. JPEG2000 performs well in the ground-based HS images and HEVC provides an outstanding performance in the airborne type of HS images. It is worth noting HEVC is more appropriate for high-definition HS images, also it can better maintain the brightness scale of HS images due to the increase of the prediction directions. HEVC allows high-precision reconstruction along different directions. As JPEG2000 is based on the wavelet transform, the
wavelet coefficients of subbands describe the spatial frequency characteristics of the horizontal and vertical directions of the spectral bands. The wavelet coefficients of different subbands reflect the characteristics of different spatial resolution of HS spectral bands. Through multi-level wavelet decomposition, wavelet coefficients can represent both high-frequency information and low-frequency information well. In this way, JPEG2000 can keep more HS image details. That probably led to the superior performance of JPEG2000. 3D-SPIHT technique generates a relatively higher compression ratio compared with ASWDR and EZW. It’s probably because 3D-SPIHT can diminish intra-band redundancy to a smaller degree. JPEG2000 is suitable for intra-band compression method, both HEVC and 3D-SPIHT has intraband and inter-band prediction. Because SPIHT sorting from the root coefficients to their descendants. Sorting coefficients and transmission process is repeated recursively. For 3D-SPIHT algorithm, this process will require even higher memory. Besides, to adopt a floating point type of wavelet coefficient may cause a certain degree of image distortion which is not ideal for lossless compression. The future research about HS images is quite exciting. VTT Technical Research Centre of Finland has created the world's first hyperspectral mobile device by converting an iPhone camera into a new kind of optical sensor. This will bring the new possibilities of low-cost spectral imaging to consumer applications. Consumers will be able to use their mobile phones, for example, to sense food quality or monitor health [67]. Due to rapid HS image sensors development, HS videos are available in recent years. Imec (world-leading research in Nanoelectronics) presents a prototype HS video acquisition [78]. HS video compression extends to process 4 dimensional data, this is another interesting area to explore. Recently, a breakthrough in precision farming is to use multispectral crop monitoring. This technology combined a compact mapping drone with a five-band sensor and is a reliable platform to capture high-quality multi-spectral data for agricultural applications [79]. Extensively used HS videos will require further improvements in compression techniques. Acknowledgments. Special thanks to Anthony Dusselaar, Rosemarry Dusselaar, Jetze Dusselaar, and Xiao Xixun for their support.
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