Artifacts Removal in JPEG Decompression via learned dictionary and Total variation regularization.

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1 Artifacts Removal in JPEG Decompression via learned dictionary and Total variation regularization. Chithra P S Niyas Ibrahim Dr.A Neela Madheswari Department. of Computer Department of Information Department. Of Information Science&Engineering Technology Technology KMEA Engineering College. KMEA Engineering College KMEA Engineering College Aluva, kerala, India Aluva, Kerala, India Aluva, Kerala, India. chithrapsarasan@gmail.com niyasibrhim07@gmail.com neela.madheswari@gmail.com Abstract JPEG Compression scheme is a widely used form of compression technique which provides good compressed result. However, the standard JPEG decompression result usually contains some artifacts, such as Blocking artifacts, Gibbs artifacts, Staircase artifacts, etc. Compression artifacts are a noticeable distortion of media caused by lossy compression technique. This artifact usually occurs when the compression ratio is high. This paper proposes a novel strategy for reducing artifacts with the help of a two step algorithm. First step is dictionary learning and second step is solving total variation regularization for decompressed images. Primal duel algorithm is used to solve total variation decompression model more effectively. The resultant image is enhanced with JPEG dithering technique. Numerical experiments are performed to demonstrate that the proposed method outperforms DicTv method (Dictionary Total variation) in the measure of PSNR (peak of signal to noise ratio) and, SSIM metric (structural similarity). stages. We know that image is an array of pixels; first step is to divide this image into non-overlapping block of size 8x8. Next step is to apply forward discrete cosine transform (DCT) on each block. The output of DCT is frequency domain or certain DCT coefficients. Second stage is divide the above DCT coefficient by Quantization table and rounded to nearest integers. Final stage is applying lossless compression (Entropy encoding) to produce the compressed result. Fig 1 shows the standard JPEG compression and Decompression schemes. Original image DCT Quantizati on Keywords DCT; Total variation; JPEG Compression; Quantization. I. INTRODUCTION There are various applications based on images such as Image processing [14][15][16][17], Data or System security[18] and so on. This Paper concentrates towards one of the applications of Image processing i.e. Image compression. JPEG compression process is one of the most popular lossy compression schemes that centers around discrete cosine transform. The Compression techniques are divided into two types, one is Lossy compression and other is lossless compression. When we compress an image the decompressed image is not exactly same as that of input image such type of compression is known as lossy compression or non-reversible coding. Here the information loss occurs at the time of compression and it is difficult for reconstruction purpose. In lossless compression, the reconstructed image, after compression, is exactly identical to the original image. The Lossless compression can only achieve a modest amount of compression. Lossless compression techniques are usually used for medical imaging, Clip art, Technical drawing etc.jpeg compression [1] technique usually consists of three Encoded result Entropy encodin Decoded Inverse Dequantiz image DCT ation Entropy decodin Fig. 1.UpperDiagram: JPEG Compression; Lower Diagram: JPEG Decompression [1] The main problem of image compression is that certain round of error will occur at the time of quantization. In 2014 International Conference on Current Techniques in Medical Image Analysis(CUTECMIA 14) 13

2 particular, when compression ratio is high, more visible artifacts appear in decompressed images. A compression artifact is a noticeable distortion of media caused by the application of compression. Artifacts can appear when we perform block-based coding for quantization, as in JPEGcompressed images. Artifacts are of different types such as Blocking artifacts, Gibbs artifacts, Ringing artifacts, etc. Compression technique divides the original image into sub images are called blocks. Each block is transformed and the selected large coefficients are quantized and then stored/transmitted. The drawback is that the discontinuities at the block boundaries are visible. Such types of artifacts are termed as blocking artifacts. Ringing artifacts are the artifacts that appear as spurious signals near sharp transitions in a signal. Visually, they appear as bands or "ghosts" near edges. In staircase artifacts, edges spanning multiple blocks can be distorted. This happens especially in the case of diagonal edges, this type of edges have a staircase like character. In literature, different techniques are introduced to reduce these types of artifacts. There are inevitably artifacts in JPEG decompressed images in Fig 2(b) where 2(a) is the original input image and 2(b) is the decompressed image. The main aim of the paper is to improve the quality of decompressed images. Fig. 2. (a) Input image (b) Compressed image with high compression ratio. II. MOTIVATION Artifacts are the important problem in decompressed images. There are different techniques used to reduce the artifacts in compressed and decompressed domain. Weighted total variation model [1] as a popular and effective image prior model in regularization-based image processing fields, such as deblurring, super-resolution (SR) and denoising. It make use of SR algorithm, in which the spatial information distributed in different image regions is added to constrain the SR process. It is used to identify the spatial property of each pixel, and a weighted parameter determined by the difference curvature information is added to constrain the regularization strength of the TV regularization at each pixel. The experimental results show that the proposed spatially weighted TV SR algorithm not only efficiently reduces the artifacts produced with a TV model in fat regions of the image, but also preserves the edge information. Bredies and Holler [4] proposed a total variation decompression model for reducing artifacts. It is one of the energy minimization methods; a fast primal dual algorithm is developed to solve this model effectively. The important drawback of this method is different artifacts (i.e., staircase artifacts) are introduced, especially for the images with more textures. The total variation model is shown using the equation (1). Where Ringing artifacts removal in digital restored images using multi resolution edge map is proposed by Sinyoung Jun and Sangjin Kim [8]. Multi-resolution edge map is used for reducing ringing artifact in digitally restored images. The reducing ringing artifacts performance depends on the accurate classification of the local features in the image. The discrete wavelet transform (DWT) is another technique which is effective both spatial and frequency characteristics of an image. In the case of DWT analysis, it is shown that the ringing artifacts can be reduced by using multiple level edge maps, which provide enhanced matching to local edges. The experimental results, shows that DWT analysis can reduce ringing artifacts with minimized edge degradation. Tuan Q. Pham and Lucas J. van Vliet [9] propose blocking artifacts removal by a hybrid filter method. It performs edge preserving and low pass filtering of degraded images. The deblocking result is then constructed from soft binding of the two filtered outputs. Soft binding avoids hard segmentation of images into monotone and textured region thereby reducing boundary artifacts after segment unification. The I- dimensional oriented filter is efficient type of filter that enhances lines and edges in textured region. It significantly reduces the compressed images with short amount of time. III. SYSTEM ENVIRONMENT The system is implemented using Matlab version The entire work is split into following phases: 1. Image Compression 2. Dictionary Learning 3. Total variation Regularization 4. JPEG dither JPEG compression method is one of the successful compression schemes that produce a good compressed result. But decompression result of standard JPEG decompression consists of some visible artifacts such as blocking artifacts, gibbs artifacts, etc.for reducing these artifacts proposed 2014 International Conference on Current Techniques in Medical Image Analysis(CUTECMIA 14) 14

3 system make use of two techniques such as dictionary learning and total variation regularization. A. Image Compression First step of proposed system is image compression. DCT is the first step for image compression. It can be performed in 8x8 blocks. The pixel value of black and white image ranges from 0 to 255.Here pure black pixel is represented by 0 and pure white pixel is represented by 1. But DCT is designed to work on pixel ranges from -128 to 127; the original block is leveled off by subtracting 128 from each entry. Next step is applying DCT in each block results a frequency domain. Our 8x8 DCT coefficients are ready for Quantization. Useful feature of image compression is varying level of image compression and quality is obtained through the selection of specific quantization matrices. Quality level ranges from 1 to 100 where 1 gives poorest image quality and highest compression and 100 gives best quality and lowest compression. The standard quantization matrix is multiplied by (100-quality level)/50 if quantization level greater than 50. The standard quantization matrix is multiplied by 50/Quality level if quality level less than 50. The resultant scaled quantization matrix is rounded to positive integer rounded from 1 to 255. The Quantization technique is achieved by dividing each element in DCT coefficient by the corresponding element in quantization matrix which is rounded to nearest integers. Next step is entropy encoding to produce the compressed result. For performing quantization, a default JPEG quantization matrix is used. It is given in Fig Fig. 3. Default quantization matrix In Fig 4(a) shows an input image before compression and Fig 4(b) shows the compressed image when the quality of compression q=50/3. B. Dictionary learning Second step of our proposed model is learning Dictionary. A fixed dictionary is learned via K-SVD [5], and the restored images are then derived based on the learned dictionary. It is a two-step iterative method.the first step is to use the orthonormal matching pursuit (OMP) [1][3]algorithm to update the encoding coefficients.the second step is to use SVD to update the dictionary. The combined equation used for dictionary learning is given below: The combination of the first and second terms requires the restored image is a sparse linear combination of elements in the dictionary. The advantage of this model is that we determine a sparse representation of the restored image in the learned dictionary so that the restored image can keep the features of the original input image. Are the encoding coefficients and dictionary determined in the above step. is related to the representation of their stored image in the dictionary. The term is used to require the encoding coefficients vector to be sparse. Fig 5(a) shows the compressed image and Fig 5(b) is the learned dictionary by using KSVD method.here we are finding the sparse representation from the compressed image. Fig. 5. (a) Compressed image (b) Dictionary learned by KSVD Fig. 4. (a) input image (b) Compressed image when q=50/3. C. Total variation regularization Based on the ideas of sparse representation[5][6] and energy minimization methods, this paper propose a novel decompression method via a learned dictionary in order to decompress JPEG images with less artifacts and better textures. We develop a reducing artifact model for restoring JPEG decompressed images in the discrete setting. We present an efficient algorithm, where we do one dictionary learning step and then solve the TV model with fixed dictionary. A fast primal-dual algorithm is proposed to solve this model effectively. The first order primal dual algorithm to update the restored image [1] is given in Algorithm International Conference on Current Techniques in Medical Image Analysis(CUTECMIA 14) 15

4 Algorithm1: 1. Initialization: parameters 2. Compute iteratively for k=1, 2 L. 3. Compute (5) Where (6) Parameter λ can be obtained by using following formula [4]:- 4. Compute 5. Output ul I refers identity operator and K* and K be the conjugate operator. Where λ is defined as [4] For quantized DCT coefficients taking nonzero values [3]:- (7) Where (10) Fig.7. (a) Restored image (b) Image after applying JPEG Dithering signal Fig. 6. (a) Compressed image (b) Learned Dictionary (c) Restored image. Fig 6(a) shows the compressed image and Fig 6(b) shows the learned dictionary by K-SVD denoising method. Fig 6(c) shows the restored image after applying primal dual algorithm. Here most of the artifacts are reduced after applying primal dual algorithm. D. JPEG Dither. To the obtained output from TV method, we can add a dithering noise [3] signal in the DCT domain, in order to improve the quality of the image. Anti-forensic artifacts can be reduced if the dithered image is requantized using the same quantization matrix used in the original JPEG compression step. Dithering signal [3][4] can be designed by following way:- Fig 7(a) shows the restored image and Fig 7(b) shows the resultant image after applying dithering signal in DCT domain. This type of dithered signal completely enhances image. Image enhancement can be identified by checking its PSNR and SSIM values. IV. SYSTEM EVALUATION For evaluating the quality of compressed image this paper proposes two parameters which is listed below:- 1. PSNR value. From this value we can ensure the output is received with the same or good quality at the receiver side International Conference on Current Techniques in Medical Image Analysis(CUTECMIA 14) 16

5 2. SSIM metric. The average SSIM index is used to evaluate the overall image quality. The larger the value is, the better the restoration result. Assuming that and are the 11-by-11 sub-images centered at the i th pixel location of two images u and respectively, the local SSIM index is defined by: 50/3 SSIM Parrot PSNR SSIM Pepper PSNR SSIM Glomeruli PSNR SSIM Where Cameraman PSNR SSIM House PSNR SSIM Parrot PSNR SSIM Pepper PSNR SSIM V. EXPERIMENTAL RESULT DicTv method make use of image patch size 6X6 (ie. m=6) and redundancy factor is chosen to be 3 emperically. So the number of atoms of the dictionary is set to be 6 2 X 3 = 108. Here the number of iterations are set to be 20. The parameters σ,τ=. In table 1, different images such as Cameraman,House,Parrot,Pepper,Glomeruli are evaluated by setting q value as 10,50/3,25 and the PSNR and SSIM metric of these images are obtained with DicTv method and JPEG dithering method. From the evaluation result it is clear that the proposed JPEG Dither provide better average PSNR and SSIM values. TABLE.I. PSNR AND SSIM VALUE WHEN q=10,50/3,25. JPEG q Name DicTv DITHER Cameraman PSNR SSIM House PSNR SSIM Parrot PSNR SSIM Pepper PSNR SSIM Glomeruli PSNR SSIM Cameraman PSNR SSIM House PSNR Glomeruli PSNR SSIM Average PSNR SSIM VI. CONCLUSION JPEG image decompression approach is used to reduce artifacts such as Gibbs artifacts and blocking artifacts via the learned dictionary, which outperforms various existing methods such as total variation and weighted total variation decompression methods. A two step algorithm is used for that purpose which include dictionary learning step and total variation regularization. Dictionary learning is performed by OMP algorithm and KSVD toolbox. Image restoration is done by solving TV model. JPEG Dithering is used to remove antiforensic noise in DCT domain. The anti-forensic noise can be completely canceled if the dithered image is requantized using the same quantization matrix used in the original JPEG compression step. Experimental result shows that JPEG Dithering enhances the resultant image obtained from total variations which provide better PSNR and structural similarity. REFERENCES [1] Huibin Chang, Michael K.Ng, Tieyong Zeng Reducing Artifacts in JPEG Decompression via a learned dictionary, IEEE transaction on signal processing Vol.62,No 3,Februvary [2] Giuseppe Valenzise, Marco Tagliasacchi, M, Stefano Tubaro, Revealing the Traces of JPEG Compression Anti-Forensics, IEEE Transactions in Information Forensics and security, Vol.8, No. 2, Februvary [3] Matthew C. Stamm, Steven K. Tjoa, W. Sabrina Lin, and K. J. Ray Liu, AntiForensics of JPEG compression, in Proc. Int. Conf. Acoustics, Speech, and Signal Processing, Dallas, TX, Apr International Conference on Current Techniques in Medical Image Analysis(CUTECMIA 14) 17

6 [4] M. C. Stamm, S. K. Tjoa, W. S. Lin, and K. J. R. Liu, Undetectable image tampering through JPEG compression anti-forensics, in Proc. Int. Conf. Image Process., Hong Kong, Sep. 2010, pp institute of medical science &research centre,june ,ICMCMM 2013,PP no-38. [5] Michal Aharon, Michael Elad, and Alfred Buckstein, K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation, IEEE transactions on signal processing.vol. 54, NO. 11, November [6] Michael Elad and Michal Aharon, Image Denoising Via Sparse and Redundant Representations over Learned Dictionaries, IEEE Transactions on image processin, VOL. 15, NO. 12, December [7] L. Ma, L. Moisan, J. Yu, and T. Zeng, A dictionary learning approach for Poisson image deblurring,,ieee Trans. Med. Imag., Mar [8] C. Jung, L. Jiao, H. Qi, and T. Sun, Image deblocking via sparse representation, Signal Process. Image Commun., vol. 27, no. 6, pp , [9] OMP and K-SVD implementations are made available at Matlab Accessed on [10] Sangjin Kim, Sinyoung Jun, Eunsung Lee1, Jeongho Shin, Ringing artifacts removal in digital restored images using multi resolution edge map, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 2, No. 4, December, [11] Tuan Q. Pham and Lucas J. van Vliet, Blocking artifacts removal by a hybrid filter method, Quantitative Imaging Group,Faculty of Applied Sciences, Delft University of Technology,Lorentzweg 1, 2628 CJ Delft, The Netherlands. [12] Antonin Chambolle, and Thomas Pock, A first-order primal-dual algorithm for convex problems with applications to imaging, J. Math. Imag. Vis., vol. 40, no. 1, pp , [13] Thanh-Ha Do, Salvatore Tabbone, Oriol Ramos Terrades, Document Noise Removal using Sparse Representations over Learned Dictionary,Author manuscript, published in The 13th ACM Symposium on Document Engineering (2013) DOI : / [14] Nejma M A, Smitha Spatial and Transform domain technique for image fusion In proceeding of national conference on Information and communication technologies (NCIT 2014) held at Baselios pauloseii Catholics college, Baselios Mount, Piravom, [15] Rejina A S,Selin M& A.Neela madheswari, Performance analysis of nonlinear decision based filter and weighted median-e filter for salt & pepper noise removal Proceeding of IRACST International journal of computer science &Information technology and security(ijcsits),issn: ,vol 3,No.5, pp.no: , Oct 2013 [16] Rejina A S,Aswathy Anda Chacko, Implementation of image denoising method with an Improved Cluster,International conference on Mathematics Statistics &computer Engineering ICMSCE-2013,pp No , Aug [17] Neena susan Varghese,Sushimita sahoo& A.Neela Madheswari, Fuzzy Gaussian mixture modelling for Image contrast Enhancement International Conference of Mathematical modelling in computer Management&medical sciences,department of computer application,macfast and pushpagiri Institute of medical science&research center, ICMAMM 2013,pp No-38,June [18] Semeena Latheef,Leya Elizabeth sunny, Authentication of sharing based method with data repairing capability and enhancing securing by chaotic scramling,international conference of mathematical sciences,department of computer application MACFAST and pushpagiri 2014 International Conference on Current Techniques in Medical Image Analysis(CUTECMIA 14) 18

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