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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010 An Efficient Vector Quantization Method for Image Compression with Codebook generation using Modified K-Means S. Sathappan, Associate Professor of Computer Science, Erode Arts and Science College, Erode-638 009. Tamil Nadu. India. E-Mail : devisathappan@yahoo.co.in Abstract— With the growth of internet and multimedia, the codebook and produces the quantized image, called as compression techniques have become the thrust area in the fields of reconstructed image. In order to attain low bit rate, many VQ computers. Image compression is a technique of efficiently coding schemes, have been used in the past literature such as side- digital image to reduce the number of bits required in representing match VQ (SMVQ) [6], classified SMVQ (CSMVQ) [7] and image. Many image compression techniques presently exist for the Gradient based SMVQ (GSMVQ) [8]. compression of different types of images. In this paper Vector Quantization based compression scheme is introduced. In this Codebook scheme a low bit rate still image compression is performed by compressing the indices of Vector Quantization and residual C={c1,c2..cn} codebook is generated. The indices of VQ are compressed by exploiting correlation among image blocks, which reduces the bit per index. A residual codebook similar to VQ codebook is generated that LBG / Modified represents the distortion produced in VQ. Using this residual K-Means codebook the distortion in the reconstructed image is removed, VQ Indices thereby increasing the image quality. The proposed technique Image Blocks combines these two methods and by replacing the Modified k-means Encoder algorithm for LBG in the codebook generation. Experimental results on standard image Lena show that the proposed scheme can give a reconstructed image with a higher PSNR value than all the existing (a) image compression techniques. Codebook Keywords—Image compression, Vector Quantization, Residual Codebook, Modified K-Means C={c0,c2..cN-1} I. INTRODUCTION LBG / Modified K-Means I MAGE compression is a method of efficiently coding digital image, to reduce the number of bits required in representing image. Its purpose is to decrease the storage Indices VQ Decoder Reconstructed Image space and transmission cost while maintaining good quality. VECTOR Quantization [1] has been found to be an efficient (b) technique for image compression in the past decade. VQ compression system mainly contains two components: VQ Fig. 1 (a) VQ Encoder (b) VQ Decoder encoder and decoder as shown in Fig.1. In VQ technique [2] [3], the input image is partitioned into Even though, SMVQ, CSMVQ, GSMVQ and JSPVQ are a set of non-overlapping image blocks the low bit rate schemes, they require high encoding time than that of VQ method. In this paper, an efficient low bit rate of size 4x4 pixels each and a clustering algorithm, for example image compression scheme is proposed based on VQ that Linde–Buzo–Gray (LGB) algorithm [5] and Modified K- makes use of compression of indices of VQ and residual Means [2]. The Modified K-Means algorithm is used in the codebook with modified k-means clustering instead of LGB. proposed technique, to generate a codebook This scheme attains low bit rate and better image quality than for the given set of image blocks. The previous VQ methods. codebook C comprises a set of representative image blocks called codewords. The VQ encoder discovers a closest match The rest of the paper is presented as follows: in section II, codeword in the codebook for each of the image block and the the literature survey is presented. In section III the proposed index of the codeword is transmitted to VQ decoder. The compression scheme is described. Performance of the decoder phase has the following functionalities. VQ decoder proposed system is evaluated in section IV and section V replaces the index values with the respective codewords from concludes the paper. 206 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010 II. LITERATURE SURVEY et al. applied IVQ to image restoration [13], where the encoder does not need a codebook except for some parameters. The Somasundaram et al, [1] presented a novel vector codebook at decoder is learned on image pairs consisting of an quantization approach and also explained various VQ original image and its diffraction-limited counterpart. Several schemes, such as side-match VQ (SMVQ), classified SMVQ follow-up work is reported in [14][15]. (CSMVQ) and Gradient based SMVQ (GSMVQ). In which SMVQ uses the high correlation existing between neighboring The goal of quantization is to encode the data from a blocks to achieve low bit rate and master codebook C is used source, with some loss, so that the best reproduction is to encode image blocks in the first column and first row in obtained. Vector quantization (VQ) achieves more advance. The other image blocks are encoded, utilizing the compression then scalar quantization [14], making it useful for correlation with the neighboring encoded image blocks. Let x band-limited channels. The algorithm for the design of optimal be the input image block for the compression system, and u VQ is commonly referred to as the Linde-Buzo-Gray (LBG) and l be the upper and left neighboring codewords algorithm, and it is based on minimization of the squared-error respectively. Let the size of the given image block size be k = distortion measure. The LBG algorithm starts with an initial m× n. The side-match distortion of a codeword Y can be codebook and iteratively partitions the training sequence into defined as: the Voronoi regions to obtain a new codebook that produces a (1) lower distortion. Once the final codebook is got, it can be used on new data outside the training sequence with the optimum nearest neighbor rule. If the training sequence is sufficiently long, it yields good performance for future data produced by According to their side-match distortions of all codewords the source. SMVQ sorts the codewords and then selects NS codewords with smallest side-match distortions from the master book C In the paper by M.Antonini, et al. [18], images have been of size N to form the state codebook SC, where NS < N. A coded using two-level wavelet decomposition with VQ of the best-match codeword Yi is selected to encode an image block resulting coefficients. A multiresolution codebook has been x from NS codewords and the corresponding index is coded in designed with noise-shaping bit allocation among the various log2NS bits. Thus, the SMVQ reduces the bit rate of VQ. Since subbands. The test image has been coded at a rate of 0.78bpp, mean square error caused by state codebook is higher than that achieving a PSNR of 32.1dB. In the paper by Gersho and of master codebook, SMVQ degrades the image quality and Ramamurthy [19], images have been compressed using also it requires long encoding time. Classified side-match unstructured VQ, achieving a bitrate of 0.5 – 1.5bpp. Ho and vector quantization [7] (CSMVQ) is an efficient low bit rate Gersho [19] have used multistage VQ for progressive image image compression technique which produces relatively high coding, with a PSNR of 30.93dB at 0.36bpp using 4 stages. quality image. It is a variable rate SMVQ and makes use of R.L.Claypoole et al. [21] have coded images using nonlinear variable sized state codebooks to encode the current image wavelet transform via lifting and obtained 30.5dB at 0.6bpp. block. The size of the state codebook is decided based on the An adaptive lifting scheme with perfect reconstruction has variances of left codewords and upper codewords that predict been used in [21]. the block activity of the input blocks. Also, CSMVQ uses two master codebooks, one for low detail blocks and another for high detail blocks. Another variant, gradient-based SMVQ [8] III. METHODOLOGY (GSMVQ) has been proposed, in which gradient values are used instead of variance values to predict the input vector. The compression scheme consists of two components, Another low bit rate VQ, called Jigsaw-puzzle vector compression of indices and generation of residual codebook. quantization (JPVQ) [9] was proposed,in which an input block These two are explained in this section. can be coded by the super codebook, the dynamic codebook or the jigsaw-puzzle block. The jigsawpuzzle block is 3.1. Compression of Indices constructed dynamically using four-step side match prediction The Index compression step has the following procedure. technique. When the image blocks are vector quantized, there likely to exist high correlation among the neighboring blocks and hence Interpolative vector quantization, first proposed explicitly among the corresponding codeword indices. Therefore, if by Gersho in [12], introduces dimension reduction to indices are coded by comparing with the previous indices, traditional VQ. The codebook in the encoder is learned on further reduction in the bit rate can be achieved. In Search downsampled vectors and the codebook in the decoder on Order Coding (SOC) [11], a simple searching scheme is high-dimension vectors. Except for the difference on followed to find a match for the current index from the dimension, the two codebooks have the same number of previous indices. The search order SO is defined as the order representative vectors and structure. VQ encoder maps down in which the current index is compared with the previous the sampled inputs to a set of scalar indices and VQ decoder indices. reproduces high-dimension inputs by received indices. David 207 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010 8 6 9 12 1 Get the first index generated by the VQ encoder and transmit Current index as such. 7 3 2 4 10 2. Get the next index generated by VQ Encoder. Compare this 5 1 index with the previous indices according SO 3. if SO = 1, code it as “00” and go to the step 2 else if SO = 2, code it as “01” and go to the step 2 else go to the next step. 4 if index value ≤ (ind_val (SO = 1) + J) and index value ≥ ─ (ind_val (SO = 1)+J) Fig. 2 Searching Order { if ind_val (SO =1) = ind_val (SO=2) code it as “10” followed by log (2 * ) 2 J bits The SO used in [11] is given in Fig.2. The label “1” else indicates the highest searching priority, “2” denotes the second code it as “100” followed by log (2 * ) 2 J highest priority and so on. In order to limit comparisons of the bits. } current index with previous indices, the searching range (SR) go to step 2. is fixed. The SR is defined as the number of previous indices else to be compared with current index. In this paper Search Order if index value ≤ (ind_val (SO = 2) + J) and [11], SR is taken as 10, which gives the lower bit rate than index value ≥ ─ (ind_val (SO = 2)+J) other SR. code it as “101” followed by log (2 * ) 2 J bits In this method, the index of the codeword of a block is and go to step 2. encoded exploiting the degree of the similarity of the block else with previously encoded upper or left blocks. When the degree code it as “11” followed by its original index and of similarity of the current block with one of the two goto step 2. previously encoded blocks is high, the index of the codeword of the block is encoded using the index of the neighboring Decoding of the compressed indices is done by reversing the codeword. I.e. the codeword index of the current block and above coding steps. that of the neighboring blocks are same. If the degree of similarity of the block with the neighboring blocks is not high, 3.2. Construction of Residual Codebook (RC) it is assumed that the closest match codeword of the current The residual codebook can be represented block may be nearer to the codewords of the neighboring as . Residual codebook is blocks. For example, if one of the two neighboring blocks constructed using absolute error values caused by VQ method, codeword’s index is ‘N’, the closest match codeword of the in the residual codebook construction, the image blocks that block to be encoded may lie between (N-J) th codeword and are less similar to their closest match codewords found in the (N+J) th codeword in the codebook, where J is any arbitrary codebook are taken into account. Less similarity blocks will number. So the index can be coded in log2( 2*J ) bits. This increase distortion than high similarity blocks in the idea is based on the property existing in the codebook design reconstructed image. Residual codeword (RYi) for a less using LBG algorithm with splitting technique. In the splitting similarity image block is constructed by comparing it with its technique, bigger size codebook is generated by splitting each closest match codeword. The collection of residual codewords codeword of the smaller codebook into two. The size of the RYi, RYi+1… is called residual codebook. Similarity of an codebook is always in powers of two (2M → 2(M+1)). Hence, image block x with its closet match codeword Yi is determined relatively similar two image blocks may have same closest based on minimum distortion rule (α) between them. If the match codeword in Jth position at codebook of size 2M and at mean square error (α) of an image block is greater than a codebook of size 2(M+1), one of the two image blocks may have predefined threshold value (σ), then the block is taken as less its closest match codeword at Jth place in the codebook and similarity block. other block’s codeword may be in (J+1)th place. The other Let be a k-pixels image block and non-similar blocks are encoded using their original index value. In this scheme, examining the resemblance of a block be a k-pixels closest match codeword, with its left and upper blocks is not required to encode the then the α is defined as: index of the block. The above description is the idea behind (2) our VQ indices compression scheme. In order to implement this idea, the index to be coded is compared with previous indices according to the SO given in Fig.2 and SR is fixed as 2 in this scheme. Let 1, 2,..,12 be the SO and ind_val (1), The steps used for constructing residual codebook are ind_val (2),..ind_val(12) be the indices values of the SO = given below. 1,2,…12. The following steps is used to encode VQ index. 208 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010 Step 1: An image which is to be compressed is decomposed into a set of non-overlapped image blocks of 4x4 pixels. Step 2: A codebook is generated for the image blocks using LBG algorithm. Step 3: Pick up the next codeword Yt from the codebook C and find its all closest match less similarity image blocks(X) where pv (*) is the pre assigned value of the corresponding found out using (2) from the given set of image blocks and bit in the residual sign bit plane and rrv (*) is the respective construct residual codeword RYt using the following equation. reconstructed residual value of the bit in the residual sign bit (3) plane. where k represents the number of elements in the codeword Yt and the image block Xi respectively and m denotes the number of less similarity image blocks that are closer to the codeword Yt. Repeat the step 3 until no more codeword exists in the codebook. Since residual codeword RYi is constructed only Fig. 3 Bits encircled are used for prediction for less similarity image blocks, some of the codewords Yi may not have their respective residual codewords, i.e; these After reconstructing the residual codeword, each value of codewords may not have less similarity image blocks. In the residual codeword is added to respective value of the residual codebook construction, only absolute values of the closest match codeword of the block Since the residual sign residuals of the less similarity image blocks are used. The sign bit plane for each image block has only eight bits, alternate information for each less similarity image block is preserved residual values in the residual codeword RYt are dropped and and is called residual sign bit plane. In encoding phase, for it also reduces the cost of storing residual codebook. The each less similarity image block, pixels of the block are dropped residual values are predicted from the neighboring subtracted from the corresponding pixel values of the residual values as given above. codeword Yi, then sign values (positive or negative) of the residual values of that block, called residual sign bit plane, are 3.3. Modified K-Means Algorithm to Replace LBG preserved. To reduce the bits needed for residual sign bit Initial Cluster Centers Deriving from Data Partitioning plane, only alternate bits are stored and others are dropped The algorithm follows a novel approach that performs data based on the assumption that there exists correlation among partitioning along the data axis with the highest variance. The neighboring bits. The bits used for prediction is shown in approach has been used successfully for color quantization [9]. Fig.3. In the decoding process, the bits of the residual sign bit The data partitioning tries to divide data space into small cells plane of a block are replaced with the respective residual or clusters where intercluster distances are large as possible values of the residual codeword from the residual codebook and intracluster distances are small as possible. (RC) with appropriate sign. The residual values of the dropped For instance, Suppose ten data points in 2D data space are bits are predicted from neighboring residual values using given. The goal is to partition the ten data points into two following steps. disjoint cells where sum of the total clustering errors of the two cells is minimal. Suppose a cutting plane perpendicular to X-axis will be used to partition the data. Let C1 and C2 be the first cell and the second cell respectively and and be the cell centroids of the first cell and the second cell, respectively. The total clustering error of the first cell is thus computed by: (4) and the total clustering error of the second cell is thus computed by: (5) Where ci is the ith data in a cell. As a result, the sum of total clustering errors of both cells is minimal. The partition could be done using a cutting plane that passes through m. Thus 209 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010 Fig. 4 Graphs depict the total clustering error, lines 1 and 2 represent the total clustering error of the first cell and second cell, respectively, Line 3 represents a summation of the total clustering errors of the (6) first and the second cells A parabola curve shown in Fig. 4 represents a summation of Thus the total clustering error of the first cell and the second cell, represented by the dash line 2. Note that the lowest point of the parabola curve is the optimal clustering point (m). At this (7) point, the summation of total clustering error of the first cell and the second cell are minimum. Since time complexity of locating the optimal point m is O(n2), the distances between adjacent data is used along the X- m is called as the partitioning data point where |C1| and |C2| axis to find the approximated point of n but with time of O(n). are the numbers of data points in cluster C1 and C2 Let be the squared Euclidean distance of respectively. The total clustering error of the first cell can be adjacent data points along the X-axis. minimized by reducing the total discrepancies between all data If i is in the first cell then . On the one in first cell to m, which is computed by: (8) hand, if i is in the second cell then The task of approximating the optimal point (m) in 2D is The same argument is also true for the second cell. The total thus replaced by finding m in one-dimensional line. clustering error of second cell can be minimized by reducing the total discrepancies between all data in second cell to m, which is computed by: (9) Fig. 5 Illustration of the ten data points on a one-dimensional line and the relevant Dj The point (m) is therefore a centroid on the one dimensional where d(ci,cm) is the distance between m and each data in line (as shown in Fig. 5), which yields each cell. Therefore the problem to minimize the sum of total (10) clustering errors of both cells can be transformed into the problem to minimize the sum of total clustering error of all data in the two cells to m. Let and a centroidDist can be computed The relationship between the total clustering error and the clustering point is illustrated in Fig. 4, where the horizontal- axis represents the partitioning point that runs from 1 to n (11) where n is the total number of data points and the vertical-axis represents the total clustering error. When m=0, the total clustering error of second cell equals to the total clustering error of all data points while the total clustering error of first It is probable to choose either the X-axis or Y-axis as the cell is zero. On the other hand, when m=n, the total clustering principal axis for data partitioning. However, data axis with error of the first cell equals to the total clustering error of all the highest variance will be chosen as the principal axis for data points, while the total clustering error of the second cell is data partitioning. The reason is to make the inter distance zero. between the centers of the two cells as large as possible while the sum of total clustering errors of the two cells are reduced from that of the original cell. To partition the given data into k cells, it is started with a cell containing all given data and partition the cell into two cells. Later on the next cell is selected to be partitioned that yields the largest reduction of total clustering errors (or Delta clustering error). This can be described as Total clustering error of the original cell – the sum of Total clustering errors of the two sub cells of the original cell. This is done so that every time a partition on a cell is performed, the partition will help reduce the sum of total clustering errors for all cells, as much as possible. 210 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010 The partitioning algorithm can be used now to partition a 5. if (α ≤σ ), the current block is encoded as “0”. given set of data into k cells. The centers of the cells can then else be used as good initial cluster centers for the K-means the current block is encoded as “1” followed by algorithm. Following are the steps of the initial centroid interpolated residual sign bitplane which is computed as predicting algorithm. described in section 3.2. 1. Let cell c contain the entire data set. 6. Repeat the step 4 until no more blocks exist in the 2. Sort all data in the cell c in ascending order on each image. attribute value and links data by a linked list for each attribute. The decoding of the compressed images is done by reversing 3. Compute variance of each attribute of cell c. Choose an the above said steps and residual block to be added is attribute axis with the highest variance as the principal axis for reconstructed for each less similarity block as described in partitioning. section 3.2. 4. Compute squared Euclidean distances between adjacent data along the data axis with the highest variance IV. EXPERIMENTAL RESULTS and compute the To evaluate the proposed technique experiments are carried 5. Compute centroid distance of cell c: out on standard gray scale images using a Pentium-IV computer running at 1.60 GHz under Windows XP. Three images of 512 x 512 pixels in size are used. Codebook is generated using Modified K-Means algorithm for all the Where dsumi is the summation of distances between the methods. Codebook is also generated with LBG [5] for adjacent data. comparison. For this scheme, a codebook of size 64 is used. 6. Divide cell c into two smaller cells. The partition Performances of the above algorithms are evaluated in terms boundary is the plane perpendicular to the principal axis and of bit rate (bits per pixel) and peak signal-to-noise ratio passes through a point m whose dsumi approximately equals (PSNR) given by: to centroidDist. The sorted linked lists of cell c are scanned and divided into two for the two smaller cells accordingly 7. Calculate Delta clustering error for c as the total clustering error before partition minus total clustering error of its two sub cells and insert the cell into an empty Max heap where MSE (mean squared error) is defined as: with Delta clustering error as a key. 8. Delete a max cell from Max heap and assign it as a current cell. 9. For each of the two sub cells of c, which is not empty, perform step 3 - 7 on the sub cell. where xi and yi denote the original and the encoded pixel 10. Repeat steps 8 - 9. Until the number of cells (Size of values and n is the total number of pixels in an image. Bit rate heap) reaches K. including overhead bits (i.e bits need to store codebook) for 11. Use centroids of cells in max heap as the initial cluster different threshold values ranging from 50 to 2000 for Lena, centers for K-means clustering Camera man and Pepper. 3.4. The Proposed Algorithm The performance of proposed scheme is evaluated with The proposed scheme combines compression of VQ the existing techniques for different gray- scale images of size indices and residual codebook. The steps used in this 512x512 and is given in the table I. J is set to 4 for the compressor are as follows proposed scheme. From table I, it can be observed note that 1. An image which is to be compressed is decomposed proposed method with the modified k-means instead of LBG into a set of non-overlapped image blocks of size 4x4 pixels. has an improvement in coding the VQ indices. 2. A codebook is generated for the image blocks using Modified K-Means algorithm. TABLE I 3. Construct a Residual Codebook (as described in section PERFORMANCE OF PROPOSED METHOD VQ WITH CODEBOOK 3.4) for those image blocks (less similarity blocks) whose α is SIZE 64 USING LBG, K-MEANS AND MODIFIED K-MEANS IN greater than σ . CODING STANDARD GRAY SCALE IMAGES OF SIZE 512 X 512 4. Pick the next image block (current block) and find its EACH closest match codeword in the codebook. Calculate mean Modified VQ LBG K-Means square error α for the image block using equation (2) and Images K-Means bits/index (bits/index) (bits/index) index of the codeword is encoded using VQ indices (bits/index) compression scheme presented in section 3.1. Lena 6 3.92 3.88 3.47 Cameraman 6 4.08 3.99 3.32 Peppers 6 3.72 3.66 3.20 211 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010 REFERENCES Table II shows the comparison of the PSNR values for the Lena, Camera man and Pepper images of 512x512 bits when [1] K.Somasundaram, and S.Domnic, "Modified Vector Quantization Method for Image Compression", World Academy of Science, compressed with combine VQ method and Codebook using Engineering and Technology 19 2006, pp:128-134 LBG, K-Means and Modified k-Means algorithm. [2] S. Deelers, and S. Auwatanamongkol, "Enhancing K-Means Algorithm with Initial Cluster Centers Derived from Data Partitioning along the Data Axis with the Highest Variance", International Journal of Computer Science Volume 2 Number 4, pp:247-252 TABLE II [3] R. M. Gray, “Vector quantization,” IEEE Acoustics, speech and Signal COMPARISON OF PSNR VALUES FOR THREE STANDARD Processing Magazine, pp. 4-29, 1984. IMAGES K- Modified [4] M. Goldberg, P. R. Boucher and S. Shlien, “Image Compression using VQ LBG adaptive vector quantization,” IEEE Transactions on Communication, Images Means K-Means bits/index (dB) Vol. 34, No. 2, pp. 180-187, 1986. (dB) (dB) [5] Y. Linde, A. Buzo and R. M. Gray, “An algorithm for vector quantizer Lena 6 31.60 32.51 34.65 design,” IEEE Transactions on Communication, Vol. 28, No. 1, 1980, pp. 84 – 95. Cameraman 6 30.24 31.25 33.84 [6] T.Kim, “Side match and overlap match vector quantizers for images,” Peppers 6 31.44 32.26 34.20 IEEE Trans. Image. Process., vol.28 (1), pp.84-95, 1980. [7] Z.M.Lu, J.S Pan and S.H Sun, “Image Coding Based on classified sidematch vector quantization,” IEICE Trans.Inf.&Sys., vol.E83-D(12), pp.2189-2192, Dec. 2000. [8] Z.M.Lu, B.Yang and S.H Sun, “Image Compression Algorithms based on side-match vector quantizer with Gradient-Based classifiers,” IEICE Trans.Inf.&Sys., vol.E85-D(9), pp.1414- 1420, September. 2002. [9] Chia-Hung Yeh, “Jigsaw-puzzle vector quantization for image compression” , Opt.Eng Vol.43, No.2, pp. 363-370, Feb-2004. [10] C.H.Hsieh, and J.C Tsai, “Lossless compression of VQ index with search order Coding,” IEEE Trans. Image Processing, Vol.5, No. 11, pp. 1579- 1582, Nov. 1996. [11] Chun-Yang Ho, Chaur-Heh Hsieh and Chung-Woei Chao, “Modified Search Order Coding for Vector Quantization Indexes,” Tamkang Journal of Science and Engineering, Vol.2, No.3, pp. 143- 148, 1999. [12] Gersho, “Optimal nonlinear interpolative vector quantization”, IEEE trans. on communication, vol. 38, pp 1285-1287, 1990. [13] D. G. Sheppard, A. Bilgin, M. S. Nadar, B. R. Hunt, M. W. Marcellin, “A vector quantization for image restoration”, IEEE trans. on Image Fig. 6 Comparison of PSNR values for three standard images Processing, vol. 7, pp119-124, 1998. [14] R. Nakagaki, A. K. Katsaggelos, “A VQ-based blind image restoration From Fig. 6 it is observed that this scheme gives PSNR algorithm”, IEEE trans. on Image Processing, vol. 12, pp 1044-1053, values of 34.65db, 33.84db and 34.20 db for Lena, Camera 2003. man and Peppers respectively. From this it can be observed [15] Y. C. Liaw, W. Lo, Z. C. Lai, “Image restoration of compressed image that the proposed approach produces better result than all the using classified vector quantization”, Pattern Recognition, vol. 35, pp329-340, 2002. existing methods. [16] M.A. Cody, The Fast Wavelet Transform, Dr. Dobb’s Journal, pp. 16- 28, April 1992. V. CONCLUSION [17] R.C. Gonzalez, R.E. Woods, Digital Image Processing, Pearson Education Pvt. Ltd., New Delhi, 2nd Edition. The rapid increase in the range and use of electronic [18] M. Antonini, M. Barlaud, P. Mathieu, I. Daubechies, Image Coding imaging justifies attention for systematic design of an image using Wavelet Transform, IEEE Transactions on Image Processing, Vol. compression system and for providing the image quality 1, No. 2, pp. 205-220, April, 1992. [19] A.Gersho, B.Ramamurthy, Image Coding Using Vector Quantization, needed in different applications. There are a lot of techniques Proceedings of IEEE International Conference On Acoustics, Speech available for image compression. In this paper, a new gray and Signal Processing, pp. 428-431, May 1982. scale image compression scheme is proposed which gives [20] Y.Ho, A.Gersho, Variable-Rate Multistage VQ for Image Coding, better image quality and low bit rate. This scheme is based on Proceedings of IEEE International Conference On Acoustics, Speech and Signal Processing, pp.1156-1159, 1988. VQ method and employs residual codebook to improve image [21] R.L.Claypoole, Jr., G.M.Davis, W.Sweldens, R.G.Baraniuk, Nonlinear quality and compression of VQ indices to lower the bit rate. Wavelet Transforms for Image Coding via Lifting, IEEE Transactions Experimental results on standard images show that the on Image Processing, Vol. 12, No.12, pp. 1449 –1459, Dec. 2003. proposed scheme gives better PSNR values and low bit rate than previous methods with codebook generation using LBG and kmeans. Since this scheme uses smaller codebook, it gives faster compression than the other two schemes. 212 http://sites.google.com/site/ijcsis/ ISSN 1947-5500

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