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  1. 1. ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 2, April 2012 MODIFIED POINTWISE SHAPE-ADAPTIVE DCT FOR HIGH-QUALITY DEBLOCKING OF COMPRESSED IMAGES Chandan Singh D. Rawat, Rohan K. Shambharkar, Sukadev Meher from digital camera manufacturers and software developers. Abstract— Blocking artifact is one of the most annoying As a matter of fact, the classic JPEG still dominates theartifacts in the image compression coding. In order to improve consumer market and the near-totality of pictures circulatedthe quality of the reconstructed image several deblocking on the internet is compressed using this old standard.algorithms have been proposed. A high-quality modified imagedeblocking algorithm based on the shape-adaptive DCT Moreover, the B-DCT is the work-horse on which even the(SA-DCT) is shown. The SA-DCT is a low-complexity latest MPEG video coding standards rely upon. There are notransform which can be computed on a support of arbitrary convincing indicators suggesting that the current trend isshape. This transform has been adopted by the MPEG-4 about to change any time soon. All these facts, together withstandard and it is found implemented in modern video the ever growing consumer demand for high quality imaging,hardware. This approach has been used for the deblocking of makes the development of advanced and efficientblock-DCT compressed images. In this paper we see modifiedpointwise SA-DCT method based on adaptive DCT threshold post-processing (deblocking) techniques a very actual andcoefficient instead of constant DCT threshold coefficient used relevant application area.by the original pointwise SA-DCT method. Extensivesimulation experiments attest the advanced performance of the In this paper a modified pointwise SA-DCT methodproposed filtering method. The visual quality of the estimates is for the restoration of B-DCT compressed grayscale images ishigh, with sharp detail preservation, clean edges. Blocking proposed. It is based on the pointwise shape-adaptive DCTartifacts are suppressed while salient image features are (SA-DCT) [14] approach and its characterized by a highpreserved. quality of the filtered estimate. Index Terms— Anisotropic, DCT threshold coefficient. The SA-DCT [1, 2] is a generalization of the usualDeblocking, SA-DCT. separable block-DCT (B-DCT) which can be computed on a support of arbitrary shape. It is obtained by cascaded application of one-dimensional varying-length DCT I. INTRODUCTION transforms first on the columns and then on the rows that constitute the considered support. Thus, it retains the same The new JPEG-2000 image compression standard solved computational complexity of the B-DCT. The SA-DCT hasmany of the drawbacks of its predecessor. The use of a been originally developed for coding non-rectangular imagewavelet transform computed globally on the whole image, as patches near the border of image objects, in such a way toopposed to the localized block-DCT (B-DCT) (employed e.g. minimize the stored information and to avoid the ringingby the classic JPEG), does not introduce any blocking artifacts (Gibbs phenomenon) that would appear inartifacts and allows it to achieve a very good image quality correspondence with strong edges. Because of its loweven at high compression rates. Unfortunately, this new complexity, the near-optimal de-correlation and energystandard has received so far only very limited endorsement compaction properties (e.g. [1], [5]), and its backward compatibility with the B-DCT, the SA-DCT has been included in the MPEG-4 standard [3], where it is used for the coding of image segments that lie on the video-object’s Chandan Singh D. Rawat, Phd Scholar, Electronics andCommunication Engineering Department, National Institute of Technology,. boundary. The recent availability of low-power hardwareRourkela, Orrisa, India, 9029067260. SA-DCT platforms (e.g. [2], [6]) makes this transform an appealing choice for many image-and video-processing Rohan K. Shambharkar, M.E. Student, Electronics and tasks.Telecommunication Engineering Department V.E.S.I.T Mumbai, India,9920835523. The first attempt to use of SA-DCT for image Sukadev Meher, Professor and Head, Electronics and Communication denoising was reported by the authors in [7]. The originalEngineering Department, National Institute of Technology, Rourkela,Orissa, India, Tel: +919437245472 version of the method has been adapted for image All Rights Reserved © 2012 IJARCSEE 134
  2. 2. ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 2, April 2012deblocking. These algorithms demonstrate a remarkable shown. The developed anisotropic estimates are highlyperformance, typically outperforming the best methods sensitive with respect to change points, and allow to revealknown to the authors. The SA-DCT estimates have also been fine elements of images from noisy observations.shown to achieve one of the highest subjective visual quality[8]. The paper is organized as follows. In the sections 2 to 5a brief overview on the basic anisotropic LPA-ICI inconjunction with SA-DCT method [14] is shown. In section 6we see the proposed modified pointwise SA-DCT method Fig. 1. Anisotropic LPA-ICI. From left to right: Sectorial structure of thebased on adaptive DCT threshold coefficient. In Section 7 the anisotropic neighborhood achieved by combining a number of adaptive-scaleadaptation of the denoising algorithm to deblocking [14] is directional windows; some of these windows selected by the ICI for the noisyseen and to relate the quantization table with the value of the Lena and Cameraman images [14].variance to be used for the filtering. The final Section isdevoted to extensive experimental results by considering III. SHAPE-ADAPTIVE DCTdifferent types of quantization tables, several level ofcompression, for grayscale images. SA-DCT [1, 2] is computed by cascaded application of 1-D varying-length DCT transforms first on columns and II. ANISOTROPIC LPA-ICI then on rows that constitute the considered region as shown in Fig 2. This approach concerns normalization of transformNoisy observations z of the form is given by, and subtraction of the mean and which have a fundamental impact on the use of the SA-DCT for image filtering. z(x) = y(x) + η(x)(1)where y is the original image, isindependentGaussian white noise, x is a spatial variable belonging to theimage domain . we restrict ourself to grayscaleimages (and, thus, scalar functions). Here only a brief overview on the original methoddeveloped for image denoising is shown and refer the readerto [14] (and references therein) for a more formal, complete,and rigorous description. The use of a transform with a shape-adaptive support Fig. 2. Illustration of the shape-adaptive DCT transform and its inverse.involves actually two separate problems: not only the Transformation is computed by cascaded application of 1-D varying-lengthtransform should adapt to the shape (i.e. a shape-adaptive DCT transforms, along the columns and along the rows [14].transform), but the shape itself must adapt to the imagefeatures (i.e. an adaptive shape). The first problem has found A. Orthonormal Shape-Adaptive DCTa very satisfactory solution in the SA-DCT transform [1, 2].The second problem is essentially application-dependant. It The normalization of the SA-DCT is obtained bymust be noted that conventional segmentation (or normalization of the individual 1-D transforms that are usedlocal-segmentation) techniques which are employed for for transforming the columns and rows. In terms of their basisvideo processing (e.g. [10]) are not suitable for degraded elements, they are defined as(noisy, blurred, highly compressed, etc.) data. In thisapproach, the SA-DCT in conjunction with the anisotropicLPA-ICI technique [11, 12, 13] is used. The approach isbased on a method originally developed for pointwise (2)adaptive estimation of 1-D signals [12, 13]. The technique Here, L stands for the length of the column or row to behas been generalized for 2-D image processing, where transformed. The normalization in (2) is, indeed, the mostadaptive-size quadrant windows have been used [16]. natural choice, since in this way all the transforms used areSignificant improvement of this approach has been achieved orthonormal and the corresponding matrices belong to theon the basis of anisotropic directional estimation [7, 11]. orthogonal group. Therefore, the SA-DCT—which can beMultidirectional sectorial- neighborhood estimates are obtained by composing two orthogonal matrices—is itself ancalculated for every point. Thus, the estimator is anisotropic orthonormal transform. A different normalization of the 1-Dand the shape of its support adapts to the structures present in transforms would produce, on an arbitrary shape, a 2-Dthe image. In Fig. 1, some examples of these anisotropicneighborhoods for the Lena and Cameraman images are 135 All Rights Reserved © 2012 IJARCSEE
  3. 3. ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 2, April 2012transform that is nonorthogonal (for example, as in [1] and the neighborhood is the octagon[2], where and cm=2/L for m>0). constructed as the polygonal hull ofHere is the orthonormal SA-DCT transformobtained for a region , where . Such neighborhoods are shown and are in Fig. 4.function spaces and indicates the domain of thetransform coefficients. is the inversetransform of . The thresholding (or quantization) operatoris indicated as γ. Thus, the SA-DCT-domain processing ofthe observations on a region U is written as . From theorthonormality of T and the model (1) follows that Fig. 4. The anisotropic neighborhood is constructed as the polygonal hull of , where is again the adaptive-scale kernels supports. One-dimensional ―linewise‖ kernels areGaussian white noise with variance σ2 and zero mean. used [14]. V. THRESHOLDING IN SA-DCT DOMAIN B. Mean Subtraction There is an adverse consequence of the normalization (2). An illustration of the SA-DCT-domainEven if the signal restricted to the shape is constant, the hard-thresholding,reconstructed is usually non constant. In [5] this behavior is performed according to (2) is given in Fig. 5.termed as ―mean weighting defect,‖ and it is proposed thereto attenuate its impact by applying the orthonormal SA-DCTon the zero-mean data which is obtained by subtracting fromthe initial data its mean. After the inversion, the mean isadded back to the reconstructed signal (3)where is the mean of z on U. IV. POINTWISE ANISOTROPIC LPA-ICI + SA-DCT The anisotropic adaptive neighborhoods defined bythe LPA-ICI as supports for the SA DCT, as shown in Fig. 3 Fig. 5. Hard thresholding in SA-DCT domain. The image data on anis used. By demanding the local fit of a polynomial model, arbitrarily shaped region is subtracted of its mean. The zero-mean data is thenthe presence of singularities or discontinuities within the transformed and thresholded. After inverse transformation, the mean istransform support cab be avoided. In this way, it can be added back [14].ensured that data are represented sparsely in the transformdomain, significantly improving the effectiveness of A. Thresholding in SA-DCT domain: Local Estimatethresholding. For every point x Є X, a local estimate of the signal y is constructed by thresholding in SA-DCT domain as in (3) (4) where γx is a hard thresholding-operator based on the universal threshold,Fig. 3. From left to right: Detail of the noisy Cameraman showing anadaptive-shape neighborhood determined by the Anisotropic LPA-ICIprocedure and the image intensity corresponding to this region before andafter hard thresholding in SA-DCT domain [14]. (5) A. Fast implementation of the LPA-ICI anisotropic B. Thresholding in SA-DCT domain: Global Estimate neighborhoods All the local estimates (4) are averaged together using Narrow 1-D ―linewise‖ directional LPA kernels adaptive weights (7) that depend on their local variances andgh, k h{1, 2,3,5,7,9} on the size of the corresponding adaptive-shape regions. are used for k = 8 directions, and afterthe ICI-based selection of the adaptive-scales All Rights Reserved © 2012 IJARCSEE 136
  4. 4. ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 2, April 2012 where TU is the orthonormal SA-DCT transform for a region U and γ is the thresholding coefficient and is its mean.(6) From the above relation we conclude that is dependent on γ (or γx) and . We propose to use value of γx dependent on MSE (Mean Squared Error) of image and MAX (Maximum Absolute Difference).(7) First we calculate the value of MAX and MSE. Several experiments were carried out using Lena, Peppers and Barbara image of size 512 × 512 on MATLAB platform on a C. Wiener filtering in SA-DCT domain: Local Estimate 1.6 GHz Intel(R) CPU. For B-DCT quantization, if the value Once a global estimate of the signal is available, it is used of MAX and MSE is less than 90 then thresholdingas a reference signal in order to perform Wiener filtering in coefficient is 0.9. If the value of MAX is greater than 90 andSA-DCT domain which is given by, the value of MSE is between 100 to 120 or greater than 250 then thresholding coefficient is 0.9. If the value of MAX is greater than 90 and the value of MSE is greater than 120 or less than 80 then the thresholding coefficient is 1.01. For(8) JPEG compression if the value of MSE is less than 90 then the thresholding coefficient is 0.9 or else it is 1.01. Thus in our modified case the DCT threshold coefficient DCTthrCOEF = 1.01 or 0.9 depending on MSE and MAX. D. Wiener filtering in SA-DCT domain: Global Estimate All the local Wiener estimates (8) are averaged together VII. POINTWISE SA-DCT FOR B-DCT ARTIFACTSusing adaptive weights (10) that depend on the size of the REMOVALcorresponding adaptive-shape regions. More sophisticated models of B-DCT-domain quantization noise have been proposed by many authors, here we model this degradation as some additive Gaussian white noise. In this section we restrict our attention to the(9) grayscale/single-channel case and thus assume the observation model z=y+n (13) where y is the original (non-compressed) image, z its(10) observation after quantization in B-DCT domain, and n is independent Gaussian with variance σ2, n (·) ∼N (0,σ2). We estimate a suitable value for the variance σ2 directlyVI. PROPOSED MODIFIED POINTWISE SADCT ALGORITHM from the quantization table Q = [qi,j]i,j = 1,…,8 using the following empirical formula: From (4), the threshold coefficient for DCT for localestimate is calculated as (14) γx = DCT threshold coefficient × This formula uses only the mean value of the nine table(11) entries which correspond to the lowest-frequency DCT harmonics (including the DC-term) and has beenwhere σ is the standard deviation and is an experimentally verified to be quite robust for a wide range ofadaptive-shape neighbourhood as shown in Figure 3. different quantization tables and images. A higher The author has used DCT threshold coefficient compression obviously corresponds to a larger value for theDCTthrCOEF = 0.925 (constant). We propose a more variance.adaptive method. The reconstructed image yU which is The σ2 which is calculated by (14) is not an estimate ofrestricted to the shape z|U (Figure 3) from hard thresholding is the variance of compressed image, nor it is an estimate of thegiven by variance of the difference between original and compressed images. Instead, it is simply the variance of the white Gaussian noise n in the observation model (13). It is the variance of some hypothetical noise which, if added to the(12) original image y, would require- in order to be removed - the same level of adaptive smoothing which is necessary to 137 All Rights Reserved © 2012 IJARCSEE
  5. 5. ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 2, April 2012suppress the artifacts generated by the B-DCT quantization therein) in order to simulate various types of B-DCTwith the table Q. compression. For identifying the considered quantization tables, the first row of each table is shown below: Q1(1···8,1) = [50 60 70 70 90 120 255 255], Q2(1···8,1) = [86 59 54 86 129 216 255 255], Q3(1···8,1) = [110 130 150 192 255 255 255 255]. The values of the standard deviation σ corresponding to these three tables - calculated using formula (4) - are 12.62, 13.21,Figure 6: Details of the JPEG-compressed grayscale Lena (Q=6,PSNR=28.24dB) and Pointwise SA-DCT estimate (PSNR=29.8671dB) and and 22.73, respectively.of the corresponding modified Pointwise SA-DCT estimate(PSNR=29.8679dB). The estimated standard deviation for this highly In terms of image degradation, they correspond to a mediumcompressed image is σ=17.6. to high compression level, similar to what can be obtained by using JPEG with Q = 11 (Q1), Q = 9 (Q2), or Q = 5 (Q3).Figures 6, and 7 shows the JPEG compressed grayscale Lenaimage obtained for two different compression levels (JPEG In Table 1 we present results for deblocking from B DCTquality Q=6 and Q=15) as used in original SADCT method quantization performed using these specific quantization[14] and the corresponding SA-DCT filtered estimates and tables. We compare the results obtained by the SA-DCTour modified estimates. For these two cases the estimated algorithm against the best results obtained by any of thestandard deviations are σ=17.6 and σ=9.7. methods [17, 18, 19, 20, 21, 22], as reported in [17]. We use Lena, Peppers and Barbara image of size 512 × 512 for comparison our modified SA-DCT algorithm against the VIII. EXPERIMENTAL RESULTS original SA-DCT algorithm. The results are in favor of our proposed technique. Extensive simulation experiments were carried out.Consequently, we present comparative numerical results Further positive results are shown in Table 2 for the casecollected in two separate tables. The two tables contain of deblocking from JPEG-compression. In this second tableresults for grayscale images obtained using particular comparison is shown between SA-DCT against the bestquantization tables found in the literature (Table 1) and JPEG result obtained by any of the methods [23, 24, 25, 26, 27, 19],(Table 2). as reported in [23] and we compare the modified pointwise SA-DCT method against the original pointwise SA-DCT method. Also in this comparison, our modified Pointwise SA-DCT method is found to be superior to all other methods. IX. CONCLUSION We proposed a new method of selecting DCT threshold coefficient depending on the MSE (Mean Squared Error) of the image and MAX (Maximum Absolute Difference) instead of taking it constant as in the original pointwiseFigure 7: Details of the JPEG-compressed Lena (Q=15, PSNR=31.95dB) andPointwise SA-DCT estimate (PSNR=33.2144dB) and of the corresponding SA-DCT method. Our modified pointwise SA-DCTmodified Pointwise SA-DCT estimate (PSNR=33.2148dB). The estimated approach with adaptive DCT threshold coefficient givesstandard deviation for this compressed image is σ=9.7. better result than the original pointwise SA-DCT method with constant DCT threshold coefficient. Three quantization tables - usually called Q1, Q2, and Q3- have been used by many authors (e.g. [15] and references All Rights Reserved © 2012 IJARCSEE 138
  6. 6. ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 2, April 2012 Table 1: PSNR (dB) comparison table for restoration from B-DCT quantization for three different quantization matrices. The values of best results correspond to any of the methods[17, 18, 19, 20, 21, 22] as reported in [17] and the value of original Pointwise SA-DCT method is compared with our modified Pointwise SA-DCT method. Lena Peppers Barbara Best Deblocked Best Deblocked Best Deblocked Deblocked Deblocked DeblockedTable Compressed result Modified Compressed result Modified Compressed result Modified SA-DCT SA-DCT SA-DCT image from SA-DCT image from SA-DCT image from SA-DCT image image image [14] image [14] image [14] image Q1 30.70 31.63 32.1256 32.1293 30.42 31.33 32.0149 32.023 25.94 26.64 26.7903 26.8026 Q2 30.09 31.19 31.5572 31.5593 29.82 30.97 31.4408 31.4532 25.59 26.32 26.453 26.4641 Q3 27.38 28.65 29.03307 29.037 27.22 28.55 29.13359 29.1436 24.03 24.73 25.12925 25.1301 Table 2: PSNR (dB) comparison table for restoration from JPEG compression of grayscale images. The values of best results correspond to any of the methods[23, 24, 25, 26, 27, 19] as reported in [23] and the value of original Pointwise SA-DCT method is compared with our modified Pointwise SA-DCT method.Qua Lena Peppers Barbaral JPEG Best SA-DCT Modifie JPEG Best SA-DCT Modifie JPEG Best SA-DCT Modified result result result d d SA-DCT from from from [1] SA-DCT [1] SA-DCT [1]4 26.46 27.63 28.07731 28.0818 25.61 26.72 27.40472 27.4359 23.48 24.13 24.65214 24.65496 28.24 29.22 29.8671 29.8679 27.32 28.22 28.96882 28.9925 24.50 25.08 25.50203 25.51768 29.47 30.37 30.9860 30.9868 28.40 29.28 29.9023 29.9222 25.19 25.71 26.10794 26.1198610 30.41 31.17 31.8447 31.8461 29.16 29.94 30.5068 30.5213 25.79 26.27 26.61224 26.626012 31.09 31.79 32.4774 32.4786 29.78 30.47 31.0064 31.0203 26.33 26.81 27.10363 27.1174 REFERENCES [8] Van derWeken, D., E. Kerre, E. Vansteenkiste, and W. Philips, ―Evaluation of fuzzy image quality measures using [1] Sikora, T., ―Low complexity shape-adaptive DCT for a multidimensional scaling framework‖ Proc. of the 2nd Int. coding of arbitrarily shaped image segments., Signal Workshop on Video Process. and Quality Metrics for Process.: Image Comm., vol. 7, pp. 381-395, 1995. Consumer Electronics, VPQM2006, Scottsdale, AZ, [2] Sikora, T., and B. Makai, ―Shape-adaptive DCT for generic January 2006. coding of video‖, IEEE Trans. Circuits and Systems for [9] Foi, A., K. Dabov, V. Katkovnik, and K. Egiazarian, Video Techn., vol. 5, no. 1, pp. 59-62, 1995. ―Shape-adaptive DCT for denoising and image [3] Koenen, R., ―Overview of the MPEG-4 Standard‖, ISO/IEC reconstruction‖, Proc. SPIE El. Imaging 2006, Image JTC1/SC29/WG11 Doc. N3536, July 2000. Process.: Algorithms and Systems V, 6064A-18, January 2006. [4] Foi, A., V. Katkovnik, and K. Egiazarian, ―Pointwise shape-adaptive DCT as an overcomplete denoising tool‖, [10] O.Connor, N., S. Sav, T. Adamek, V.Mezaris, I. Proc. 2005 Int. TICSP Workshop Spectral Meth. Multirate Kompatsiaris, T.Y. Lu, E. Izquierdo, C. Bennström, and J. Signal Process., SMMSP 2005, pp. 164-170, Riga, June Casas, ―Region and object segmentation algorithms in the 2005. Qimera segmentation platform‖, Proc. Third Int. Workshop on Content-Based Multimedia Indexing (CBMI03), Rennes, [5] Kauff, P., and K. Schuur, ―Shape-adaptive DCT with pp. 381-388, 2003. block-based DC separation and DC correction‖, IEEE Transactions on Circuits and Systems for Video [11] Katkovnik V., A. Foi, K. Egiazarian, and J. Astola, Technology, vol. 8, no. 3, pp. 237-242, 1998. ―Directional varying scale approximations for anisotropic signal processing,‖ Proc. of XII European Signal Process. [6] Kinane, A., A. Casey. V. Muresan, and N. O.Connor, Conf., EUSIPCO 2004, pp. 101-104, Vienna, September ―FPGA-based conformance testing and system prototyping 2004. of an MPEG-4 SA-DCT hardware accelerator‖, IEEE 2005 Int. Conf. on Field-Programmable Technology, FPT. 05, [12] Katkovnik V., ―A new method for varying adaptive Singapore, December 2005. bandwidth selection‖, IEEE Trans. on Signal Proc., vol. 47, [7] A. Foi, V. Katkovnik, K. Egiazarian, and J. Astola, ―A novel no. 9, pp. 2567-2571, 1999. anisotropic local polynomial estimator based on directional [13] Goldenshluger, A., and A. Nemirovski, ―On spatial adaptive multiscale optimizations,‖ in Proc. 6th IMA Int. Conf. estimation of nonparametric regression‖, Math. Meth. Math. Signal Process., Circencester, U.K., 2004, pp. 79–82. Statistics, vol.6, pp.135-170, 1997. 139 All Rights Reserved © 2012 IJARCSEE
  7. 7. ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 2, April 2012[14] Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian, Chandan Singh D. Rawat received B. E degree in the department of ―Pointwise Shape-Adaptive DCT for High-Quality Electronics and communication engineering from Nagpur University. He Denoising and Deblocking of Grayscale and Color Images‖, also received M.E degree from Mumbai University. He is currently pursuing Phd at National Institute of Technology Rourkela, Orissa, India. IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. He is presently working as an Associate professor in VESIT, Chembur, 16, NO. 5, MAY 2007 Mumbai, India. His area of interest is Image processing.[15] Liew, A.W.C., and H. Yan, ―Blocking Artifacts Rohan K. Shambharkar received B.E. degree in Electronics and Suppression in Block-Coded Images Using Telecommunication engineering from Mumbai University. He is OvercompleteWavelet Representation‖, IEEE Trans. currently pursuing M.E. degree in Electronics and Telecommunication Circuits and Systems for Video Technology, vol. 14, no. 4, engineering from Mumbai University. His area of interest is Image pp. 450-461, April 2004. Processing. Dr Sukadev Meher received his B.Sc(Engg) in 1984. He also[16] V. Katkovnik, K. Egiazarian, and J. Astola, ―Adaptive received his M.Sc(Engg) in 1992.He received his Phd in 2005. He is window size image de-noising based on intersection of currently working as Professor and Head of Department in Electronics confidence intervals (ICI) rule,‖ J. Math. Imag. Vis., vol. and Communication engineering, National Institute of Technology, 16, no. 3, pp. 223–235, 2002. Rourkela, Orissa, India. His field of interest includes Digital Signal[17] Liew, A.W.C., and H. Yan, ―Blocking Artifacts Processing, Digital Image Processing, Circuit & Systems, and Optical Suppression in Block-Coded Images Using Overcomplete Communication. Wavelet Representation‖, IEEE Trans. Circuits and Systems for Video Technology, vol. 14, no. 4, pp. 450-461, April 2004.[18] Hsung, T.-C., and D.P.-K. Lun, ―Application of singularity detection for the deblocking of JPEG decoded images‖, IEEE Trans. Circuits and Systems II, vol. 45, no. 5, pp. 640-644, May 1998.[19] ―MPEG-4 video veriÞcation model version 18.0 (VM-18)‖, ISO/IEC JTC1/SC29/WG11, Doc. N3908, 2001.[20] Paek H., R.C. Kim, and S.U. Lee, ―On the POCS-based post-processing technique to reduce blocking artifacts in transform coded images‖,‖IEEE Trans. Circuits Sys. Video Technology, vol. 8, pp. 358-367, June 1998.[21] Yang, Y., N.P. Galatsanos, and A.K. Katsaggelos, ―Projection-based spatially adaptive reconstruction of block-transform compressed images‖, IEEE Trans. Image Process., vol. 4, no. 7, pp. 896-908, July 1995.[22] Xiong, Z., M. Orchard, and Y. Zhang, ―A deblocking algorithm for JPEG compressed images using overcomplete wavelet representations‖, IEEE Trans. Circuits and Systems for Video Technology, vol. 7, no. 4, pp. 433-437, April 1997.[23] Averbuch, A., A. Schclar, and D.L. Donoho, ―Deblocking of block-transform compressed images using weighted sums of symmetrically aligned pixels‖, IEEE Trans. Image Process‖, vol. 14, no. 2, pp. 200-212, February 2005.[24] Chen, T., H.R. Wu, and B. Qiu, ―Adaptive postÞltering of transform coefÞcients for the reduction of blocking artifacts‖, IEEE Trans. Circuits and Systems for Video Technology, vol. 11, no. 5, pp. 584-602, August 2001.[25] Rosenholts, R., and A. Zakhor, ―Iterative procedures for reduction of blocking artifacts in transform domain image coding‖, IEEE Trans. Circuits and Systems for Video Technology, vol. 2, no. 2, pp. 91-95, March 1992.[26] Yang, Y., N.P. Galatsanos, and A.K. Katsaggelos, ―Regularized reconstruction to reduce blocking artifacts of block discrete cosine transform compressed images‖, IEEE Trans. Circuits and Systems for Video Technology, vol. 3, no. 6, pp. 421-432, December 1993.[27] Marsi, S., R. Castagno, and G. Ramponi, ―Asimple algorithm for the reduction of blocking artifacts in images and its implementation‖, IEEE Trans. Consum. Electron., vol. 44, no. 3, pp. 1062-1070, August 1998.[28] ―MPEG-4 video veriÞcation model version 18.0 (VM-18)‖ ISO/IEC JTC1/SC29/WG11, Doc. N3908, 2001. All Rights Reserved © 2012 IJARCSEE 140

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