ROI Coding Techniques for SPECK Compressed colored Images Nasseer Moyasser Basheer Medical instrumentation engineering dept. Technical College in Mosul Mosul, Iraq
[email protected] Abstract: This work presents SPECK (Set Partitioned Embedded bloCK) algorithm to compress colored images. MATLAB is used as the environment for all applications. It is proposed here to transform the RGB color image to YCbCr spaces, compress each of the three components as done in grayscale SPECK, and then interlace the three outputs to form one bitstream. Also the ROI (Region Of Interest) coding is applied here for the SPECK compressed color images. The proposed ROI coding does not interfere with compression algorithm but it gives simple, efficient and easily applied ROI coding with variable PSNR (Peak Signal to Noise Ratio) for the ROI and the background of the color image. Keywords: SPECK compression algorithm, colored image ROI marking, ROI coding, DWT.
I.
INTRODUCTION
In recent years there has been an astronomical increase in the usage of computers for a variety of tasks. With the advent of digital cameras, one of the most common uses has been the storage, manipulation, and transfer of digital images. The files that comprise these images, however, can be quite large and can quickly take up precious memory space on the computer's hard drive. In multimedia application, most of the images are in color. Color images contain lot of data redundancy and require a large amount of storage space[1]. In digital true color image, each color component that is R, G, and B components, each contains 8 bit data[2]. Also color image contains lots of redundancy which will make it difficult to store and transmit. However, RGB [2] model is not suited for image processing purpose. For compression, a luminancechrominance representation is considered due to superior to the RGB representation[1]. Therefore, RGB images are transformed to one of the luminance-chrominance models, performing the compression process, and then transforming it back to RGB model because displays are most often provides output image with direct RGB model[1]. In this R, G, and B component of color image are converted to YCbCr before wavelet transform (it is the first step in image compression to transform it into another axis and it is a common procedure to perform the image transforming by the Discrete Wavelet Transform (DWT) to transform the image into frequency
Omar Salah Shakar Computer engineering dept. Mosul University, Electronics College Mosul, Iraq
[email protected] domain) is applied. Y is luminance component, Cb and Cr are chrominance components of the image. The luminance component represents the intensity of the image and looks like a gray scale version. The chrominance components represent the color information in the image[1]. In some practical situations where tight rate-control is needed (e.g., transmitting images over a time-varying low-bit rate wireless channel), it is desirable to retain the quality of the regions that are visually important, by scarifying the quality of other regions that are not critically needed. The more important region is known as the Region Of Interest (ROI)[3]. In ROI coding certain part(s) is (are) compressed with maximum available means to get best retrieved part(s) at the decoder side, while other image data outside ROI region(s) (which sometimes called the background) are of less interest regarding retrieved image quality[4]. In 1993 Shapiro [5] first employed a tree representation of wavelet coefficients for encoding of images which is known as Embedded Zero Tree (EZW). In 1996 Pearlman et al. [6] proposed an advanced version, the Set Partitioning In Hierarchical Trees (SPIHT), which is even more efficient for lossy and loseless coding of images. In 1999 Pearlman et al.[7] proposed an embedded, block-based, image wavelet transform coding algorithm of low complexity, The Set Partitioned Embedded boCK (SPECK), this image coding scheme has its roots primarily in the ideas developed in the SPIHT. In 2004 Pearlman et al.[8] suggested CSPECK (Color SPECK), which is similar method that employs SPECK (Set-Partitioning Embedded Block Coder) algorithm, to code color images by losslessly transforming the RGB image to LC (LuminanceChrominance) plane. There have been several ways to extend SPECK to colored images. For instance, Khelifi F. et al [9] selected two planes to be grouped together into the List of Insignificant Sets LIS in SPECK algorithm. Our study is based on the Color-SPECK (CSPECK), which is an easy and efficient extension of SPECK without any modifications to compress colored images, also ROI coding has been applied to these colored images compressed by CSPECK algorithm without so many modification of the algorithm. The ROI coding methods have been used, primarily designed to be used for JPEG2000, where it applied to CSPECK in both Arbitrary
and Geometrical Shaped ROI region. This gives a simple coding, effective results, and achieves the main goal. This paper is organized as follows: Wavelet Transformation of Image is described in section II. SPECK algorithm is explained in section III. Modeling and results is given in section IV. Conclusion is mentioned in section V.
4. The reconstruction process is called the inverse DWT (IDWT). If C[m,n] represents an image, the DWT (as shown in Fig 2.a) and IDWT (as shown in Fig 2.b) for C[m,n] can similarly be defined by implementing the DWT and IDWT on each dimension and separately.
II. WAVELET TRANSFORMATION OF IMAGES Wavelets[10] are mathematical functions that are used to decompose data into different frequency components, and then study each component with a resolution matched to its scale. They have advantages over traditional Fourier methods[11] in analyzing physical situations where the signal contains discontinuities and sharp spikes. Wavelets were developed independently in the fields of mathematics, quantum physics, electrical engineering, and seismic geology. Interchanges between these fields during the last ten years have led to many new wavelet applications such as image compression, turbulence, human vision, radar, and earthquake prediction[1]. The wavelet transformation[10] is a mathematical tool for decomposition. The wavelet transform is identical to a hierarchical subband filtering system[2], where the subbands are logarithmically spaced in frequency. The basic idea of the DWT for a two-dimensional image is described as follows: 1. An image is first decomposed into four parts based on frequency subbands, by critically sub sampling horizontal and vertical channels using subband filters named as LowLow (LL), Low-High (LH), High-Low (HL), and High- High (HH) sub bands as shown in Fig. 1.a.
a
b Figure 2. DWT and IDWT Transforms
III. SPECK ALGORITHM a
b
Similar to SPIHT[6], SPECK[7] consists of three stages: initialization, sorting and refinement. However, it sorts the information of wavelet coefficients in two ordered lists. List of Insignificant Sets LIS and the List of Significant Pixels LSP. At the initialization stage, a start threshold depending of the maximum value in the wavelet coefficients pyramid is defined (chosen as a power of two: T=2(n-1)) . Pixel significance in an entire set (T) of pixels and threshold selection are carried out using equation (1).
Figure 1. wavelet Transforms
(1) 2. To obtain the next coarser scaled wavelet coefficients, the subband LL is further decomposed and critically sub sampled. This process is repeated several times, which is determined by the application at hand. This process is shown in Fig 1.b. 3. Each level has various bands information such as low–low, low–high, high–low, and high–high frequency bands. Furthermore, from these DWT coefficients, the original image can be reconstructed.
where
n=[ log2( max |ci,j| ) ]
The list LSP is set as empty. Then, the image X is partitioned into two groups (see Fig. 3): set of type S which is the root of the pyramid, and set of type I which is the rest of the image[7].
Figure 3. Partitioning of image χ into stets S and I
From the standpoint of implementation, a block of set of type S is determined by the coordinates of the pixel in the topleft and the size of this block. First, set of type S is added to the LIS. In the sorting pass, the algorithm first starts to sort each block of type S in LIS by performing a significance test against the current threshold (1 or 0). A block is said significant if there is at least one coefficient in this block whose magnitude is greater than or equals the threshold. If a block of type S is significant, it is partitioned into four subsets of the same type (S0, S1, S2 and S3) as shown in Fig.4 [7] [1].
Figure 4. Partitioning of stet S.
In the LIS, this block is replaced by the resulting subsets. In the case of a significant block of size 1×1 (one pixel), its sign is coded and then its coordinates are moved to the LSP. In the same way, the set I is tested with respect to the current threshold where its spliting up produces one subset I and three subsets of type S (S1, S2and S3) as depicted in Fig.5. This significance and partitioning processes are carried out for all sets of type S (including the new ones) and the set I . Noting that, depending upon the information content of the image and the desired bit-rate of coding, the set I can disappear at a certain point [7].
Figure 5. Partitioning of stet I.
In the refinement pass, the nth most significant bit of each entry in the LSP, excluding those which have been added during the last sorting pass, are output. Then, the current threshold is divided by 2 and the sorting and refinement stages are continued until a predefined bit-rate is achieved (lossy case) or a smallest value of n (n=0 lossless case) is reached [7][1].
The decoder uses the same mechanism as the encoder. It receives significance test results from the coded bitstream and builds up the same list structure during the execution of the algorithm. Hence, it is able to follow the same execution paths for the significance tests of the different sets, and reconstructs the image progressively as the algorithm proceeds[7]. The Details of SPECK algorithms are presented in [7], also the MATLAB implementation of the SPECK algorithms are presented in [4], and the flowcharts of this algorithm presented in [12]. IV. COLOR IMAGE CODING Compression also can come from the way color is represented within pixel data values. In order to represent color within an image several values are used. There are several formats used to represent color, the two most common being RGB and Luminance/Chrominance, which both employ threevalue pixel representations. A. Color Spaces Transformation One of the advantages of this transformation is that it reduces the psychovisual redundancy in RGB image. Also, it has the important feature that the luminance component is the compatible monochrome (grayscale) version of the image. It has been shown that the human visual system (HVS) is relatively insensitive to the high frequency content of the chrominance components. Thus, these components are commonly sub-sampled to remove redundancy[13]. B. The RGB Color Space The RGB format represents a colored image in three separate parts. Each of the three samples represent the same image, just the relative proportions of red, green, and blue for each given pixel. These are primary colors so any color or shade can be produced by a combination of red, green, and blue intensities. In a digital true- color images, each color component is represent with 8 bits, and so a color is specified with 24 bits. As a result, there are 224 possible colors for the image. Furthermore, a color image usually contains a lot of data redundancy and requires a large amount of storage space[22]. C. The YCbCr Color Space The YCbCr color space is used widely in digital video. In this format, luminance (Y) information is represented by a single component, Y and color information is stored as two color-difference components, Cb and Cr represent the chrominance or color difference within the sample. Component Cb is the difference between the blue component and a reference value, and component Cr is the difference between the red component and a reference value[14]. Most importantly luminance and chrominance values are separated rather than being incorporated in the same value such as RGB.
D. RGB Space To YCbCr Space Because of a lot of data redundancy in RGB format, therefore using luminance/chrominance has major advantages for compression in comparison to RGB. Also the chrominance values can be further reduced in compression techniques while not severely altering the image as seen by a human observer. The purpose of color conversion is to reflect the color sensitivity according to the characteristics of human eyes. The color space transformed from RGB to YCbCr as the following equations[14] [18]: YCbCr = Offset + T * RGB
(2)
Where T=
and Offset =
(3)
and the reverse transformation YCbCr to RGB space is as follows[18]: RGB= T - 1 * (YCbCr - Offset)
(4)
0 0.00625893 0.00456621 Where T − 1 = 0.00456621 - 0.00153632 - 0.00318811 0.00456621 0.00791071 0 0.00625893 (Y − 16) R 0.00456621 0 * (Cb − 128) (5) G = 0.00456621- 0.00153632 - 0.00318811 0 (Cr − 128) B 0.00456621 0.00791071
To convert from RGB space to YCbCr space by using the functions that are already available in MATLAB[14][18]:
V. COLOR IMAGE COMPRESSION U SING COLOR-SPECK (CSPECK) This section investigates lossy color embedded image compression with SPECK. Following the philosophy of retaining embeddedness in color representation as has been done with SPIHT, a lossy color embedded image coding scheme has been proposed by [8] using SPECK algorithm, called Colored SPECK (CSPECK). Its performance is evaluated with respect to some of the other famous colorembedded image coders such as Predictive EZW (PEZW), SPIHT and JPEG2000[17]. In order to take advantage of the interdependencies of the color components for a given color space, the set-partitioning scheme of SPECK is extended across the color components and coding is done in such a way as to retain embeddedness [8]. A simple application of SPECK to a color image would be to code each color space plane separately as does a conventional color image coder. Then, the generated bitstream of each plane would be serially concatenated. However, this simple method would require bit allocation among color components, losing precise rate control and would fail to meet the requirement of full embeddedness of the image codec, since the decoder needs to wait until the full bitstream arrives to reconstruct and display[8]. Pearlman et. al.[13][8] suggest to treat all color planes as one unit at the coding stage, and generate one mixed bitstream so that it can be stopped at any point of the bitstream and reconstruct the color image with best quality at that bit rate. In addition, it will automatically allocate bits optimally among the color planes. By doing so, a full embeddedness and precise rate control of SPECK still maintain, this is called Color-SPECK (CSPECK). The generated bitstream of both methods is shown in Fig.8, where the first shows a conventional color bitstream, while the second shows how the color embedded bitstream is generated, from which it is clear that stopping at any point of the bitstream, can still reconstruct a color image at that bit rate as opposed to the first case[8].
>> ycbcr _ image = rgb2ycbcr(RGB_ image); And from the YCbCr space to RGB space we use : >> rgb _ image = ycbcr2rgb(YCbCr_ image); Fig.6 shows RGB image, and Fig.7 shows YCbCr image.
Figure 6. RGB Lena image
Figure 7: YCbCr format image
Figure 8. Compressed color bitstreams: conventional (top) and embedded (bottom)[8].
Pearlman et. al. [13][8] considered the color space YUV (4:2:0 format), where the chrominance U and V planes are onequarter the size of the luminance Y plane. Each color plane of the color image is separately wavelet transformed (using 9/7 filter), having its own hierarchical subband structure. Then each one of them is initially partitioned into sets S and I. An LIS is maintained for each of the three transform planes, each one initialized with the corner coordinates of its top level S.
There is just one LSP list. Now the coding proceeds similarly to the original SPECK. All noted details are given in [13][8]. VI. PROPOSED BITSTREAM ARRANGEMENT FOR FULL EMBEDDEDMENTS USING SPECK ALGORITHM To still maintain full embeddedness with precise rate control of SPECK, and generate one mixed bitstream so that we can stop at any point of the bit-stream and reconstruct the color image of the best possible quality. A new method has been proposed to compress the colored image using SPECK algorithm with the same simplicity of the SPECK used for grayscale images in [7][4][12]. The three bitstreams have been generated of each plane individually as done in original grayscale SPECK, then these bitstreams reproduce a one full mixed bitstream. The idea is to partition each output bitstream from the SPECK coder for each plane of the colored image into consecutive blocks of length N bits as packets (with 100 bit/packet). It is demanded to take the first packet of each of the three bitstreams Y, Cb, and Cr, then take the second packets of them and so on. So the first three packets are concatenated to form the beginning of the modified bitstream, the second packets are concatenated after the first three packets and so on. The following Fig.9 shows the mechanism of the embedded bitstream generation. PacketY2
PacketY1
PacketY3
PacketY4
and return each packet to the proper location in its bitstream (reversing the steps in Fig.9). Then three bitstreams (Y, Cb, and Cr) are reproduced. Finally these generated bitstreams are send to the SPECK decoder to reconstruct the three planes Y, Cb, and Cr, and then, putting back the reconstructed image to the RGB format after applying IDWT. A. MATLAB Results Matlab software is used for performing this work. The 512*512 colored test images are used after converting to YCbCr space, a DWT transformation with 8 levels is done for Y, Cb, and Cr using the MATLAB bior4.4 wavelet filter. The data is compressed by the proposed coding method without so many modifications of the SPECK algorithm[7][4][12]. The PSNR has been calculated for the reconstructed RGB image. Also the results for different truncation ratios, where, the embedded bitstream is truncated at 10%, 25%, and 40% from the total length, and the percentage of the received bitstream after the truncation are shown in table I. Figures 11,12,13, and 14 (a, b, c, and d) show the Color-SPECK compression results of the proposed method for colored test images with compression ratio, PSNR, and different truncation ratios for bit rate=0.5bpp. TABLE I. SPECK COMPRESSION RESULTS BY THE PROPOSED METHOD USING MATLAB WITH BPP=0.5.
PacketY5
Image
aaaa
bbbb cccc
dddd
eeee
……………
N
(a) : bitstream of Y color plane PacketCb1
PacketCb2
ffff
PacketCb3
gggg
hhhh
PacketCb4
Lena (512*512)
PacketCb5
iiii
jjjj
…………
N
Barbara (512*512)
(b) : bitstream of Cb color plane PacketCr1
PacketCr2
kkkk
llll
PacketCr3
PacketCr4
mmmm nnnn
PacketCr5
oooo
…………
N
(c) : bitstream of Cr color plane PacketY1 PacketCb1 PacketCr1 PacketY2 PacketCb2 PacketCr2 PacketY3 PacketCb3
aaaa
ffff
kkkk bbbb gggg
llll
Pepper (512*512)
Percentge received bitstream 5.64 % 4.70 % 3.76 % 5.64 % 4.70 % 3.76 % 5.64 % 4.70 % 3.76 % 5.64 % 4.70 % 3.76 %
Compression Ratio
PSNR (dB)
6.25 % 6.25 % 6.25 % 6.25 % 6.25 % 6.25 % 6.25 % 6.25 % 6.25 % 6.25 % 6.25 % 6.25 %
39.10 38.45 37.54 35.89 34.98 33.94 37.44 36.71 35.85 37.48 37.06 36.35
cccc hhhh ……. N
(d) : full embeddedment mixed bitstream Figure 9.
Goldhill (512*512)
Truncated bitstream Ratio 10 % 25% 40% 10 % 25% 40% 10 % 25% 40% 10 % 25% 40%
VII. REGION OF INTEREST (ROI) CODING FOR COLORED IMAGES
Mechanism of generating the full embeddedment mixed bitstream
If the bitstream is discontinued for any reason the data received by the decoder represents a combination of the bitstreams of the three layers Y, Cb, and Cr. Reconstruction of the colored image is possible according to embeddedeness principle characterizing SPECK codec, with quality depending on bit budget received. Fig.10 shows the model used for compressing color images with the proposed color image coding method implementing on SPECK algorithm. As shown in Fig.10 at the SPECK decoder the mixed embeddedment bitstream is separated into three bitstreams, this separating is done by dividing the mixed bitstream to packets,
A. ROI Coding Region-of-Interest (ROI) Coding: Often there are parts of an image that are of greater importance than others. This feature allows users to define certain ROI region(s) in the image to be coded and transmitted without information loss or with a better quality and less distortion than rest of the image[15]. The ROI functionality is important in applications where certain parts of the image are of higher importance than others [16]. In such a case, these regions need to be encoded at higher quality than the background (BG). During transmission of the image, these regions need to be transmitted first or at a higher priority, as in the case of progressive transmission.
Decompression using SPECK Algorithm
Y bitstream Cb bitstream
channel
Cr bitstream
DWT
Cr
To The
Separating the mixed bitstream into three bitstreams
DWT
Mixing the three bitstreams into one bitstream
Cb
Y Cb Cr bitstream bitstream bitstream
DWT Compression using SPECK Algorithm
Y
IDW T
Y
IDW T
Cb
IDW T
Cr
Figure 10: Proposed Method of Color Image Compression Using SPECK
a
b
c
d
Figure 10. Lena test image : Compression ratio is 6.25% , and rate is 0.5 bpp (a) Original without compression, (b) Truncation ratio 10% giving PSNR 39.10 dB, (c)Truncation ratio 25% giving PSNR 38.45 dB, and (d) Truncation ratio 40% giving PSNR 37.54 dB.
a
b
c
d
Figure 11. Barbara test image: Compression ratio is 6.25% , and rate is 0.5 bpp (a) Original without compression, (b) Truncation ratio 10% giving PSNR 35.89 dB, (c)Truncation ratio 25% giving PSNR 34.98 dB, and (d) Truncation ratio 40% giving PSNR 33.94 dB.
a
b
c
d
Figure 12. Goldhill test image : Compression ratio is 6.25% , and rate is 0.5 bpp (a) Original without compression, (b) Truncation ratio 10% giving PSNR 37.44 dB, (c)Truncation ratio 25% giving PSNR 36.71 dB, and (d) Truncation ratio 40% giving PSNR 35.85 dB.
a
b
c
d
Figure 13. Pepper test image : Compression ratio is 6.25% , and rate is 0.5 bpp (a) Original without compression, (b) Truncation ratio 10% giving PSNR 37.48 dB, (c)Truncation ratio 25% giving PSNR 37.06 dB, and (d) Truncation ratio 40% giving PSNR 36.35 dB.
B. Identifying an ROI Region Identifying and preparing the region of interest accurately is very important before coding and compressing the image data for efficient transmission or storage. First the ROI region must be defined, then preparing an ROI mask, and finally deciding to use a certain approach to perform the ROI coding[17]. In this paper the ROI region has been defined by using MATLAB, the method used here to define ROI region, to be of arbitrary or geometric shape with free hand by instructions that are already available in MATLAB. The interactive instruction ROIPOLY is used to specify an arbitrary region of interest (ROI) within an image, and IMELLIPSE instruction is used to select geometric ROI region in the original image as shown in Fig15,16. All details for these instruction found in [18][4][12]. The ROI mask defines which ROI wavelet coefficients needed to be coded perfectly because they are spread in all the DWT levels after transforming the image, see Fig.17. The ROI coefficients locations are determined through the ROI mask, therefore the ROI mask is used as a kind of look up table and this look up table must be available on both the encoder and the decoder sides[4]. In this paper the ROI mask must be available only at encoder side and no need to transmit index of the ROI coefficients to the decoder side.
Figure 14. Arbitrary ROI
encoding of arbitrary or geometrical shaped ROIs is possible without the need to inform the decoder about the ROI mask. Also the encoder and decoder are simple, since no shape encoding is required, i.e. shape is implicit at the decoder, where it can still handle ROI regions of arbitrary or geometric shapes[15]. Here in this method, all bitplanes of ROI coefficients must be scaled up over the maximum bitplane of all BG coefficients. Also the Maxshift method is a particular case of the general scaling-based method when the scaling value is so large that there is no overlapping between BG and ROI bitplanes, i.e., so the scaling value, S, must satisfy (4): S ≥ Max(M)
(4)
Where max(M) is the largest coefficient in the background region. All significant bits associated with the ROI after scaling will be in higher bitplanes than all the significant bits associated with the background. Therefore, ROI shape is implicit for the decoder in this method, and arbitrarily shaped ROI coding can be supported[20]. Then, during the embedded coding process, the most significant ROI bit planes are placed in the bitstream before any BG bit planes of the image. Thus, the ROI will be decoded, or refined, before rest of the image. Now if the encoding bitstream is truncated for any reason the ROI coefficients will possess a higher ratio of the decoded bitstream, giving ROI retrieved sections as good as possible[21].
Figure16. Geometrical ROI
Figure 18. Maxshift method.
Figure 17. ROI mask generation in DWT
C. ROI Coding Scheme In JPEG2000 image compression algorithms the ROI coding methods used depend on finding a relationship between the ROI coefficients and the BG coefficients. Where, It defines many methods such as Scaling Based, Maxshift, Most Significant Bitplanes (MSB) Shift, and others[19]. Noting that In all these methods, the scaling up is done after DWT and before bit plane coding, and at the decoder scaling down by the same amount is done. All details of these methods can be found in [4][19]. In this paper the ROI coding scheme is based on the Maxshift method, as shown in Fig.18, which is one of the general ROI scaling-based coding method. The Maxshift method take many advantages on the other schemes, when,
D. MATLAB Results The Maxshift method has been implemented in MATLAB using Color-SPECK algorithm with the proposed method as mentioned earlier in VI. Table II and Figures 19, and 20 give the experimental results for this method applied on two different 512*512 images for arbitrarily shaped ROI regions. Table III and Figures 21, and 22 give the experimental results for geometrically shaped ROI regions for other test images. The tables are prepared by using different encoding depths, the first for half of the maximum values for n, and the second deeper with another 3 values of n. this results in different values of compression in bpp for the three layers in each test image condition.
TABLE II.
n=30 Lena pepper
SHOWS PSNR, COMPRESSION R ATIO , CALCULATED BPP, AND THE ENCODING DEPTH FOR ARBITRARY SHAPED ROI CODING USING THE MAXSHIFT METHOD. Threshold level From 30–to15 From 30–to12 From 30–to15 From 30–to12
Y 0.41 0.69 0.38 0.63
Bpp Cb 0.33 0.6 0.34 0.59
Cr 0.32 0.6 0.32 0.57
a
PSNRROI
PSNRBG
Compression Ratio
ROI region Size
57.21dB 57.99 dB 58.58 dB 58.84 dB
21.37 dB 21.37 dB 19.98 dB 19.98 dB
4.47% 7.92 % 4.41% 7.52%
6.62% 6.62% 5.44 % 5.44%
b
c
Figure 19. Lena test image: (a) Original, (b) compression ratio 4.47%, and (c) compression ratio7.92%.
a
b
c
Figure 20. Pepper test image : (a) Original, (b) compression ratio 4.41%, and (c) compression ratio7.52%.
TABLE III.
Shows PSNR, Compression Ratio, calculated bpp, and the encoding depth for Geometric Shaped ROI coding Using the Maxshift METHOD. Threshold level
Goldhill Barbara
From 30–to15 From 30–to12 From 28–to14 From 28–to11
Y 0.12 0.19 0.22 0.35
a
bpp Cb 0.07 0.13 0.13 0.25
Cr 0.08 0.14 0.13 0.24
b
PSNRROI
PSNRBG
Compression Ratio
ROI region Size
23.62 dB 66.75 dB 19.64 dB 62.27 dB
3.78 dB 20.86 dB 4.63 dB 18.65dB
1.20 % 2.01 % 2.06 % 3.59 %
1.01% 1.01% 2.84 % 2.84%
c
Figure 21. Goldhill test image : (a) Original, (b) compression ratio 1.2%, and (c) compression ratio 2.01%.
a
b
c
Figure 22. Barbara test image: (a) Original, (b) compression ratio 2.06%, and (c) compression ratio 3.59%.
[7]
VIII.
CONCLUSION
Compressing color images efficiently is one of the main challenges in multimedia applications. In this paper, we made use of the CSPECK algorithm for video compression proposed by Pearlman et. al. [13][8], in compressing colored still images and instead of using the YUV color space, the YCbCr space has been used. The three color planes have been compressed independently using original SPECK algorithm to produce three bitstreams. Then those bitstreams are rearranged in a way similar to TDM technique, with packets interlaced at a certain sequence to keep the important advantage of the SPECK embeddedment. The ROI coding has been implemented in two methods, the arbitrary and the geometrical ROI shapes, with the same ROI coding methods used for grayscale images in [4] and [12]. This is achieved for each of the three layers in the (Y, Cb, and Cr) spaces. In order to simplify reconstruction for the image without using a lookup table at the decoder, the MAXSHIFT method has been chosen in this paper, and it is successfully implemented on the different color test images as shown in the results. The other ROI coding methods given in [4] and [12] can also be implemented without any modifications except for the availability of a lookup table at the decoder side. The ROI coding results show an important relationship between PSNR of the ROI region with size of the ROI region as compared to the whole image. When the ROI region is small and the compression depth is also small as in table III, Fig.21(b), and Fig.22(b), the PSNR is not good. But it becomes better when going deeper in compression as in table III, Fig.21(c), and Fig.22(c). While for rather larger ROI regions as in table II, Fig.19, and Fig.20 the PSNR is good even for low compression depths. The user is having the choice of using any method for specifying the ROI region shape and size, according to his/her demands and the type of media he is using. It is advised to use JPEG2000 ROI coding methods, which include the Maxshift method used here, for other image compression techniques, where it is expected to give good results with acceptable efforts and may not need major alterations on those techniques. REFERENCES [1]
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