# MRT based Fixed Block size Transform Coding

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1 3 MRT based Fixed Block size Transform Coding Contents 3.1 Transform Coding Transform Selection Sub-image size selection Bit Allocation Transform coding using 8 8 MRT Unique MRT Coefficients Significance of Unique MRT coefficients Encoding Decoding Results and Analysis Comparison between DCT and MRT based image compression techniques Application in images with higher resolutions Application in Color images Transform coder based on 4 4 MRT Unique Coefficients in 4 4 MRT matrices Encoding Decoding Results and Analysis Application in Color images Page 63

2 3.1 Transform Coding Transform coding techniques use a reversible, linear mathematical transform to map the pixel values onto a set of coefficients, which are then quantized and encoded. The key factor behind the success of transform-based coding schemes is due to the fact that many of the resulting coefficients, for most natural images, have small magnitudes and can be quantized (or discarded altogether) without causing significant distortion in the decoded image. Different mathematical transforms, such as Discrete Fourier (DFT), Walsh-Hadamard (WHT), and Karhunen-Loeve (KLT), have been considered for the task. For compression purposes, the higher the capability of compressing information in fewer coefficients, the better the transform; for that reason, the Discrete Cosine Transform (DCT) had become the most widely used in transform coding techniques. Figure 3.1: A Transform Coding System, (a) Encoder and (b) Decoder A typical transform coding system is shown in Figure 3.1. Transform coding algorithms usually start by partitioning the original image into sub-images or Page 64

3 blocks of small size, usually 8 8. The transform coefficients are computed for each block, effectively converting the original 8 8 array of pixel values into an array of coefficients within which certain coefficients usually contain most of the information needed to quantize and encode (and eventually perform the reverse process at the decoder s side) the image with little perceptual distortion. The resulting coefficients are then quantized and the output of the quantizer is used by a (combination of) symbol encoding technique(s) to produce an output bit stream representing the encoded image. The reverse process takes place at the decoder s side, with the obvious difference that the de-quantization stage will only generate an approximated version of the original coefficient values; in other words, whatever loss was introduced by the quantization process in the encoder stage is not reversible Transform Selection Transform coding systems based on a variety of discrete transforms have been studied extensively in section 1.5. The choice of a particular transform in a given application depends on the amount of reconstruction error that can be tolerated and the computational resources available. Compression is achieved during the quantization of the transformed coefficients and not during the transformation step. Consider an image ( ) of size N N, whose forward discrete transform, ( ) can be expressed in terms of the general relation ( ) ( ) ( ) ( ) Page 65

4 for 0 k1, k2 N-1. Given ( ), ( ) can similarly be obtained using the general inverse transform relation ( ) ( ) ( ) ( ) for 0 n1, n2 N-1. In these equations, g(n1,n2,k1,k2) and h(n1,n2,k1,k2) are called forward and inverse transformation kernels or basis functions or basis images. Y(k1,k2), for 0 k1,k2 N-1 are called transform coefficients. The forward and inverse transform kernels determine the type of transform that is computed and the overall computational complexity & reconstruction error of the transform coding system in which they are employed Sub-image size selection The sub-image size is a significant factor affecting transform coding error and computational complexity. In most applications, images are subdivided so that the correlation (redundancy) between adjacent sub-images is reduced to some acceptable level. Usually the sub-image size, N, is an integer power of 2. The latter condition simplifies the computation of the sub-image transforms. In general, both the level of compression and computational complexity increase as Page 66

5 the sub-image size increases. The most popular sub-image sizes are 4 4, 8 8, and Bit Allocation The reconstruction error associated with the transform coding is a function of the number and relative importance of the transform coefficients that are discarded as well as the precision that is used to represent the retained coefficients. In most of the transform coding systems, the retained coefficients are selected on the basis of maximum variance, zonal coding, or on the basis of maximum magnitude, called threshold coding. Zonal coding is based on the information theory concept of viewing information as uncertainty. Therefore the transform coefficients of maximum variance carry the most image information and should be retained in the coding process. Zonal coding is usually implemented by using a fixed mask for all sub-images. Threshold coding, however, is inherently adaptive in the sense that the location of the transform coefficients retained for each sub-image vary from one sub-image to another. In fact, threshold coding is the adaptive transform coding approach most often used in practice because of its computational simplicity. There are three basic ways to threshold a transformed sub-image. A single global threshold applied to all the sub-images A different threshold used for each sub-image; or The threshold is varied as a function of the location of each coefficient within the sub-image. Page 67

6 Bit allocation is the overall process of truncating, quantizing and coding the coefficients of a transformed sub-image. 3.2 Transform coding using 8 8 MRT Unique MRT Coefficients As explained in [135], the raw MRT of a signal has a considerable amount of redundancy. For a two-dimensional signal of size N N, the total number of MRT coefficients is N 3 /2, from N/2 MRT matrices of size N N. However, of these N 3 /2 coefficients, only N 2 coefficients are unique. Hence, in order to save memory space and to make it simpler, it is necessary to eliminate the redundancy in the MRT matrices and retain only the unique coefficients so that a compact MRT representation is obtained. MRT coefficients are unique for certain values of k1 and k2: for k2 = 0 and all powers of 2 up to and including N/2 and all or certain values for k1 from among 0,1,2,3,,N- 1 depending on the value of p. Thus it is sufficient to compute the MRT coefficients for these specific values of k1, k2 and p. The values of k1, k2 and p which yield unique MRT coefficients for an image block of size 8 8 are shown in Figure 3.2. Page 68 Figure 3.2: Indices k1 k2 p of unique coefficients in 8 8 MRT matrix.

7 In Figure 3.2, 000 means that the MRT coefficient at that position is formed from values of k1 = 0, k2 = 0, p = 0, and similarly for the other entries. [149] Significance of Unique MRT coefficients Since MRT coefficients are formed from simple addition and subtraction of image data, each unique MRT coefficient signifies a particular manner of combination of image data and thus a unique pattern in the image domain. Thus, the N 2 Unique MRT coefficients of an N N image block are in fact strength-indicators of N 2 different patterns in the image block. The MRT thus becomes a suitable tool for image analysis to determine the existence of N 2 well defined patterns in an image. Figure 3.3 shows the pattern associated with the MRT coefficient for ( ). The + sign indicates that the image data at that position is an addend in the formation of this MRT coefficient, and - denotes a subtrahend. 0 indicates positions of image data that do not contribute to the MRT coefficient. In image blocks with strong vertical structure, this MRT coefficient will have a significant value. The N 2 patterns can be considered to be the basis images of the MRT. An important consideration here is that the number of image data elements which contribute to MRT coefficient formation is not equal for all MRT coefficients. This number can range from N 2 downward to binary fractions of N 2. This factor may be used to perform the normalization of the MRT coefficient obtained from simple additions and subtractions of the image data. If this division is not performed in the forward transform, then it needs to be done while performing the inverse MRT [149]. Page 69

8 Figure 3.3: Template for the computation of MRT coefficient ( ) Encoding The overall encoding process is explained in Figure 3.4. The major steps in the process are explained below Figure 3.4: Block diagram of 8 8 MRT based transform coder. Partition the image into sub-images As explained in section 3.1.2, size of the sub-image is a significant factor. Since the most popular sub-image size is 8 8, the proposed method also adopts 8 8 as the size of the sub-image. So, the image to be compressed is first partitioned into 8 8 non-overlapping sub-images. Page 70

9 Application of MRT to each sub-image After the image to be compressed is partitioned into 8 8 non-overlapping blocks, 8 8 MRT is applied to each block using Equation (1.22). For calculating 8 8 MRT, N should be replaced with 8 in the Equations (1.22), (1.23) and (1.24). Application of 8 8 MRT to a sub-image generates MRT coefficients. Identification of the unique MRT coefficients The MRT maps an 8 8 sub-image into 256 coefficients, of which only 64 are unique [135]. All the remaining coefficients are just repetitions or sign changed versions of the unique coefficients. The 64 unique MRT coefficients are selected, ignoring the remaining, as per indices given in Figure 3.2. The exact reproduction of the sub-image is possible using these unique MRT coefficients. Implementation of Threshold Coding The MRT based Image compression utilizes the 64 unique MRT coefficients to represent each 8 8 sub-image, for encoding. Certain coefficients even among these unique coefficients are irrelevant (section 1.2) for reproducing the original sub-image with some acceptable error. This irrelevancy can easily be removed either by zonal coding or by threshold coding. The proposed method utilizes threshold coding, because it is inherently adaptive in the sense that the location of the transform coefficients retained from each sub-image varies from one subimage to another. The underlying concept is that, for any sub-image, the transform coefficients of largest magnitude make the most significant contribution to the quality of the reconstructed sub-image. Page 71

10 The proposed algorithm uses single global threshold, as it is the computationally simplest method and the level of compression varies from image to image depending on the number of coefficients that exceed the global threshold. This gives flexibility to the proposed algorithm as the compression levels can be varied by varying the threshold appropriately. Value of the threshold can be chosen according to the required compression level and visual quality of reconstructed image. Grouping the retained coefficients and their respective positions The MRT coefficients that overcome the threshold in each block and their respective positions in the MRT matrices are stored in two linear arrays. After the above process is applied to all the sub-images, the coefficient array contains all the retained MRT coefficients corresponding to all the sub-images and position array contains the positions of all the retained MRT coefficients. Subsequently, these two linear arrays are subjected to entropy coding. Symbol Encoding Arithmetic coding and Huffman coding are the two major symbol encoding techniques currently in use. Either of the coding techniques can be used with the proposed algorithm, as both are producing almost the same results. Figure 3.5 shows the compression process in detail Decoding The Decoding process is shown in Figure 3.6. The encoded bit stream received at the receiver is decoded and the coefficient array & the position array are Page 72

11 separated. The coefficient array contains all the retained MRT coefficients and the position array contains the positions of all the retained MRT coefficients in the MRT matrices. The MRT matrices are formed for each sub-image by placing the unique MRT coefficients from the coefficient array with reference to the positions in the position array. The sub-image is reconstructed by applying the inverse MRT (IMRT) to the MRT matrices. Finally the output sub-image is placed in the corresponding position in the image. This process is repeated for all the subimages. Figure 3.5: Illustration of coding an 8 8 sub-image Page 73

12 Figure 3.6: Decompression block diagram Results and Analysis The transform coding technique developed using 8 8 MRT is simulated on MATLAB platform over intel(r) core TM(2) CPU T GHz processor, 3GB RAM based system. The compression technique developed above is simulated using Lena ( ) image for different values of threshold, from 100 to 400 in steps of 50, and the results are tabulated in Table 3.1. Table 3.1: Bpp and PSNR obtained for 8 8 MRT based transform coder for Lena ( ) image for various threshold values Threshold % of compression bpp PSNR(dB) Page 74

13 For each threshold value, bpp, percentage of compression and PSNR are computed using Equations (1.1), (1.2) & (1.4) respectively. Figure 3.7 shows the original and reconstructed Lena images for different values of threshold. Figure 3.7: Lena ( ) (a) original and (b) (d) reconstructed after compressed using 8 8 MRT based transform coder with thresholds 100, 150 & 200 Figure 3.8: Threshold versus bpp for Lena ( ) Page 75

14 Figure 3.9: Threshold versus PSNR for Lena ( ) The compression and PSNR achieved through the proposed coding technique for different values of threshold are graphically represented in Figure 3.8 and 3.9 respectively. As the threshold increases, both compression and PSNR decrease exponentially. Hence, the required PSNR and bpp can be achieved through proper selection of the value for threshold. The relation between bpp and PSNR is shown in Figure Figure 3.10: bpp versus PSNR for image Lena ( ) Page 76

15 The algorithm is applied to other standard gray scale images for a fixed threshold value of 150 and the results obtained are shown in Table 3.2. The table shows that other standard gray scale images are also being compressed with similar results as that of Lena image. From the analysis it is clear that all types of general gray scale images can be compressed using this method. The selection of threshold is an important factor for obtaining an optimum compression with required quality of reconstructed image. Table 3.2: Performance of 8 8 MRT based transform coder for various images for a fixed threshold value Image Threshold bpp PSNR Time(s) Lena Cameraman Ic Eight Rice Bacteria Tyre The last column in Table 3.2 shows the time taken by the proposed algorithm for compressing various images. The time is bit high because of the fact that the algorithm computes all 256 MRT coefficients corresponding to each 8 8 block in the input image, even though only 64 are unique. This is a computational overhead. Page 77

16 Templates of unique MRT coefficients, shown in Figure.3.11, are used to directly compute the unique MRT coefficients from the image data itself, so as to reduce the computational overhead. It is sufficient to add/subtract/don t care the pixel values of the sub-image corresponding to 1/-1/0 in the template, to compute the 64 unique MRT coefficients of an 8 8 sub-image. The template based MRT computation also eliminates the requirement of computing the parameter z in Equation (1.22). Page 78 Figure 3.11: Templates for computing unique coefficients of 8 8 MRT

17 Table 3.3 shows the time taken by the proposed technique when the MRT computation is done through the direct method and template method. The template method is much faster than the direct method. Table 3.3: Time taken by the direct MRT and template based MRT methods to compress various images Image Time(s) (Direct) Time(s) (Template) Lena Cameraman Ic Eight Rice Bacteria Tyre Comparison between DCT and MRT based image compression techniques Presently DCT is the most common tool for block based transform coding techniques. Hence, a comparison is made between the proposed technique and DCT based transform coding. The results of the DCT based transform coding is obtained by developing an algorithm as in [58], as follows. Initially, the image to be compressed is partitioned into 8 8 non overlapping sub-images. Then for each sub-image, 128 is subtracted from all its elements and 8 8 DCT is applied to the Page 79

18 resulting sub-images. The DCT coefficients are quantized using a specially designed fixed matrix called Z [58]. Then the first 4 4 [Virtual Block Size (VBS) =4] coefficients of each DCT matrix is stored in a linear array. The linear array is entropy coded after the above process is applied to all the sub-images. The comparison between DCT and MRT based image compression techniques, presented in Table 3.4 shows that the MRT based image compression technique gives better PSNR for nearly equal or less bpp over baseline DCT based approach for some of the images. The values given in brackets, along with the name of images in column 1 of Table 3.4, is the threshold used for MRT based technique to get a compression equivalent to that of DCT based method. MRT based approach has the advantage over DCT based approach in compromising between quality and percentage of compression by varying threshold. The MRT based image compression technique, proposed here takes almost the same computation time compared to DCT based approach. Table 3.4: A comparison of 8 8 MRT and DCT based transform coders % of Compression Image bpp PSNR (db) Compression Time (s) (Threshold) DCT MRT DCT MRT DCT MRT DCT MRT Lena (195) Cameraman (200) Ic (180) Tire (170) Eight (150) Enamel (265) Cell (125) Bacteria (170) Rice (170) Nodules (140) Page 80

19 Application in images with higher resolution The above mentioned results are obtained when the proposed method is applied to images with resolution or less. The proposed technique can be applied to images with higher resolutions (ie, images with resolutions or more). The performance of the proposed technique when applied to the Lena ( ) image for different values of threshold is shown in Table 3.5. Figure 3.12 shows the original Lena ( ) image and a set of reconstructed images of Lena for values of threshold from 100 to 400 with step of 50. At higher compression ratios, threshold 350 or more, blocking artifacts are visible. Table 3.5: Performance of 8 8 MRT based transform coder for Lena ( ) image for various threshold values % of Threshold compression bpp PSNR(dB) The output of the proposed technique for various images for a fixed threshold of 150 is shown in Table 3.6. Figures 3.13 to 3.21 show the original and reconstructed version of various gray scale images for a fixed threshold of 150. Page 81

20 Figure 3.12: Lena ( ) (a) original and (b) (h) reconstructed after compressed using 8 8 MRT based transform coder with threshold varied from 100 to 400 in a step of 50 Table 3.6: Performance of 8 8 MRT based transform coder for various images for a fixed threshold Image(512x512) Threshold bpp PSNR Time(s) Lena Baboon Barbara Goldhill peppers Couple Elaine Boat Cameraman Bridge and Stream Page 82

21 Figure 3.13: Baboon ( ) (a) original and (b) reconstructed after compressed using 8 8 MRT based transform coder (threshold 150, bpp 1.32, PSNR 25.87) Figure 3.14: Barbara ( ) (a) original and (b) reconstructed after compressed using 8 8 MRT based transform coder (threshold 150, bpp 0.75, PSNR 28.58) Page 83

22 Figure 3.15: Boat ( ) (a) original and (b) reconstructed after compressed using 8 8 MRT based transform coder (threshold 150, bpp 0.63, PSNR 28.89) Figure 3.16: Bridge and stream ( ) (a) original and (b) reconstructed after compressed using 8 8 MRT based transform coder (threshold 150, bpp 1.01, PSNR 26.29) Page 84

23 Figure 3.17: Couple ( ) (a) original and (b) reconstructed after compressed using 8 8 MRT based transform coder (threshold 150, bpp 0.63, PSNR 30.11) Figure 3.18: Elaine ( ) (a) original and (b) reconstructed after compressed using 8 8 MRT based transform coder (threshold 150, bpp 0.40, PSNR 29.40) Page 85

24 Figure 3.19: Goldhill ( ) (a) original and (b) reconstructed after compressed using 8 8 MRT based transform coder (threshold 150, bpp 0.50, PSNR 29.09) Figure 3.20: Peppers ( ) (a) original and (b) reconstructed after compressed using 8 8 MRT based transform coder (threshold 150, bpp 0.53, PSNR 30.17) Page 86

25 Figure 3.21: Cameraman ( ) (a) original and (b) reconstructed after compressed using 8 8 MRT based transform coder (threshold 150, bpp 0.49, PSNR 31.13) Application in Color images Since the number of bits required to represent color is typically three to four times greater than that employed in the representation of gray levels, data compression plays an important role in the storage and transmission of color images. RGB model is preferred in this work because of its simplicity. Images represented in the RGB color model consist of three component images, one for each color. As a result, each color plane can be compressed separately just like a single gray level image. The Table 3.7 shows the results of compression for certain color images using the proposed algorithm with the value of threshold as 150. Figure 3.22 shows the original and decompressed versions of various color images like, Lena, Baboon, Crown and Peppers. Page 87

26 Table 3.7: Performance of 8 8 MRT based transform coder for color images Image bpp PSNR Lena Crown Baboon Peppers Transform coder based on 4 4 MRT The 8 8 MRT based coding technique, discussed in section 3.2, introduces blocking artifacts in the reconstructed image for higher compression ratios (i.e., for lower bpp). This will be a major problem when bpp is below 0.4. Also, the Table 3.4 shows that the performance of the 8 8 MRT based coder is lower than that of DCT based approach for certain images. These problems can be solved by choosing a smaller size for the sub-images. So the sub-image size is chosen as 4 4. The technique used in 4 4 MRT based transform coder is the same as that used in 8 8 MRT based transform coder. Page 88

27 Page 89

28 Figure 3.22: (a) (d) Original Color images and (e) (h) their reconstructed versions after compressed using 8 8 MRT based transform coder Page 90

29 3.3.1 Unique Coefficients in 4 4 MRT matrices It is quite evident from the MRT matrices that a majority of its coefficients are redundant. The MRT of a 4 4 image data contains 32 coefficients of which only 16 are unique ( Y, Y, Y, 1,1 1,2 0,0 Y 3,1 and Y 0,1, Y 0,2, Y 1,0, 4 4 MRT are shown in Figure Y 1,1, Y 1,2, Y 3,1, Y 2,0, Y 2,1, Y 2,2, Y 0,1, Y 1,0, Y 2,1 ). The templates for computing these unique coefficients of Figure 3.23: Templates for the computation of the 16 unique coefficients of 4 4 MRT Page 91

30 It is sufficient to add/subtract/don t care the pixel values of the sub-image corresponding to 1/-1/0 in the template, to compute the 16 unique MRT coefficients of a 4 4 sub-image Encoding The image is partitioned into 4 4 sub-images. The 16 unique MRT coefficients corresponding to each sub-image are computed, using the templates discussed in section 3.3.1, and placed in a 4 4 matrix. Irrelevancy reduction is performed on these unique MRT coefficients by the application of a fixed threshold. The threshold should be selected according to the required compression level and visual quality of the reconstructed image. Typically, the value of the threshold can be varied from 50 to 400 as per the requirement of the application in which this compression scheme is used. Two linear arrays are formed, for the image, from the value and position of the unique MRT coefficients that overcome the threshold. The resulting two arrays are then encoded separately using simple Arithmetic encoding. The proposed coder is shown in Figure Figure 3.24: The 4 4 MRT based transform coding scheme. Page 92

31 3.3.3 Decoding In the decoding process, both the linear arrays (coefficient and position) are decoded using Arithmetic decoding. The MRT matrices are formed, for each subimage, by placing the unique coefficients from the coefficient array with reference to the positions in the position array and deriving the redundant coefficients using the relations, Y = Y, 0,3 0,1 Y 2,3 = Y 2,1, Y = Y, 3,0 1,0 Y = Y, 1,3 3,1 Y = Y, 3,2 1,2 Y = Y, 3,3 1,1 Y =-Y, 0,3 0,1 Y =-Y, 2,3 2,1 Y =-Y, 3,0 1,0 Y =-Y, 1,3 3,1 Y =-Y, 3,2 1,2 Y =-Y, 3,3 1,1 Y 0,0 =0, Y 0,2 =0, Y 2,0 =0 and Y 2,2 =0. The output sub-images are derived through inverse MRT Results and Analysis The transform coder discussed above is simulated and the results for the Lena ( ) image under different values of threshold are shown in Table 3.8. The selection of threshold is very crucial for getting a pre-determined compression ratio. Figure 3.25(a) shows the original Lena image and Figures 3.25(b) to (i) show the reconstructed images corresponding to the threshold values 50, 100, 150,, 400 respectively. The transform coder is simulated for various images of size for a fixed threshold of 150 and the quality of the corresponding reconstructed image is represented in terms of bpp & PSNR as given in Table 3.9. Page 93

32 Table 3.8: Performance of 4 4 MRT based transform coder for Lena image for various threshold values Threshold bpp PSNR Table 3.9: Performance of 4 4 MRT based transform coder for various images for a fixed threshold Image(512x512) Threshold bpp PSNR Time(s) Lena Baboon Barbara Goldhill peppers Couple Elaine Cameraman Boat Bridge and Stream Page 94

33 As it is observed from the reconstructed images of Lena, the blocking artifacts present in the 8 8 MRT based technique are reduced to a large extent. Figure 3.25: Lena ( ) (a) original and (b) (i) reconstructed after compressed using 4 4 MRT based transform coder with bpp 0.91, 0.58, 0.44, 0.38, 0.34, 0.32, 0.31 and 0.30 respectively Application in Color images The change in block size of the transform coder from 8 8 to 4 4 is applied on color images to analyze the effect. The simulation results of the proposed Page 95

34 algorithm on various color images are tabulated in Table Figure 3.26 shows the original and decompressed versions of various color images, using the proposed algorithm, for a fixed threshold of 150. Table 3.10: Performance of 4 4 MRT based transform coder for color images Image bpp PSNR Lena Crown Baboon Peppers The analysis of the simulation results shows that this algorithm can be used to compress all types of gray scale and color images. The value of threshold determines the compromise between bpp and PSNR. A comparison between the proposed coder and the DCT based technique for a given bpp is shown in Table On analysis of the table, it is clear that the proposed 4 4 MRT based technique performs equal to or better than the DCT based technique for 6 out of 13 images. A comparison between proposed coding scheme using 4 4 MRT and 8 8 MRT is shown in Table From the table it is quite evident that the proposed 4 4 MRT gives better performances for almost all images. Page 96

35 Page 97

36 Figure 3.26: (a) (d) Original Color images and (e) (h) their reconstructed versions after compressed using 4 4 MRT based transform coder Page 98

37 Table 3.11: Comparison between 8 8 DCT and 4 4 MRT based methods Image (Threshold) bpp PSNR (DCT) PSNR (MRT) Barbara (120) Baboon (130) Couple (90) Enamel (135) Boat (85) Bridge and stream (105) Goldhill (70) Lena (83) Peppers (75) Cameraman (80) Elaine (60) Alumgrns (75) Satelite_image (105) Table 3.12: Comparison between 8 8 MRT and 4 4 MRT based methods PSNR(dB) Image 0.3 bpp 0.5 bpp 0.75 bpp Lena Baboon Peppers Goldhill Couple Cameraman Boat Elaine Page 99

38 Figure 3.27 shows the variation of PSNR with respect to bpp for the two block sizes corresponding to Lena image. A careful observation of the Table 3.12 reveals that, for all the images, at higher bit rates, 4 4 MRT based technique gives better performances and at lower bit rates 8 8 MRT based technique gives better performances. This is because, at lower bit rates, the threshold will be high. As a result, only the DC coefficient needs to be retained for each sub-image. In the case of 8 8 MRT based technique only one DC coefficient is present in an 8 8 sub-image whereas in 4 4 MRT based approach four DC coefficients will be involved for a sub-image of size 8 8 and are to be retained. Hence, the 8 8 MRT based technique performs better than 4 4 MRT based approach at lower bpp. Also for images having lesser gray scale variations, the 8 8 MRT based technique performs better than 4 4 MRT based technique. Figure 3.27: Performance comparison of 4 4 and 8 8 MRT based coders for Lena image A hybrid transform coder, utilizing a combination of 4 4 and 8 8 sub-images, can improve the performance of MRT based transform coding technique by incorporating the good features of both techniques discussed above. The next chapter deals with the implementation of such a coder. Page 100

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