Image Arbitrary-Ratio Down- and Up-Sampling Scheme Exploiting DCT Low Frequency Components and Sparsity in High Frequency Components

The development of image acquisition technology and display technology provide the base for popularization of high-resolution images. On the other hand, the available bandwidth is not always enough to data stream such high-resolution images. Downand up-sampling, which decreases the data volume of images and increases back to high-resolution images, is a solution for the transmission of high-resolution images. In this paper, motivated by the observation that the high-frequency DCT components are sparse in the spatial domain, we propose a scheme combined with Discrete Cosine Transform (DCT) and Compressed Sensing (CS) to achieve arbitrary-ratio down-sampling. Our proposed scheme makes use of two properties: First, the energy of a image concentrates on the lowfrequency DCT components. Second, the high-frequency DCT components are sparse in the spatial domain. The scheme is able to preserve the most information and avoid absolutely blindly estimating the highfrequency components. Experimental results show that the proposed downand up-sampling scheme produces better performance compared with some state-of-the-art schemes in terms of peak signal to noise ratio (PSNR), structural similarity index measurement (SSIM) and processing time. key words: image, DCT, CS, downand up-sampling, arbitrary ratio, PSNR, SSIM

[1]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[2]  Xiangjun Zhang,et al.  Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation , 2008, IEEE Transactions on Image Processing.

[3]  Sanjit K. Mitra,et al.  Fast arbitrary resizing of images in the discrete cosine transform domain , 2011 .

[4]  Gholamreza Anbarjafari,et al.  IMAGE Resolution Enhancement by Using Discrete and Stationary Wavelet Decomposition , 2011, IEEE Transactions on Image Processing.

[5]  Xuelong Li,et al.  Joint Learning for Single-Image Super-Resolution via a Coupled Constraint , 2012, IEEE Transactions on Image Processing.

[6]  Sanjit K. Mitra,et al.  Image resizing in the compressed domain using subband DCT , 2002, IEEE Trans. Circuits Syst. Video Technol..

[7]  Rabab Kreidieh Ward,et al.  A New Orientation-Adaptive Interpolation Method , 2007, IEEE Transactions on Image Processing.

[8]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[9]  Ming Li,et al.  Motion-Aware Decoding of Compressed-Sensed Video , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  Trac D. Tran,et al.  A Complexity Scalable Universal DCT Domain Image Resizing Algorithm , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[11]  Shahram Shirani,et al.  Video super resolution using contourlet transform and bilateral total variation filter , 2013, IEEE Transactions on Consumer Electronics.

[12]  Jechang Jeong,et al.  Hybrid image upsampling method in the discrete cosine transform domain , 2010, IEEE Transactions on Consumer Electronics.

[13]  Wan-Chi Siu,et al.  Robust Soft-Decision Interpolation Using Weighted Least Squares , 2012, IEEE Transactions on Image Processing.

[14]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[15]  Wan-Chi Siu,et al.  Novel DCT-Based Image Up-Sampling Using Learning-Based Adaptive ${k}$ -NN MMSE Estimation , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[16]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[17]  D RuikarSachin,et al.  Advance Neighbor Embedding for Image Super Resolution , 2013, BIOINFORMATICS 2013.

[18]  Ianwei,et al.  Compressive Video Sampling with Approximate Message Passing Decoding , 2011 .

[19]  Wan-Chi Siu,et al.  Hybrid DCT-Wiener-based interpolation via learnt Wiener filter , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[20]  Richard G. Baraniuk,et al.  Compressive Sensing , 2008, Computer Vision, A Reference Guide.

[21]  Chun-Shien Lu,et al.  Distributed compressive video sensing , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[22]  Hyun Wook Park,et al.  Arbitrary-ratio image resizing using fast DCT of composite length for DCT-based transcoder , 2006, IEEE Trans. Image Process..

[23]  Avideh Zakhor,et al.  Very low bit-rate video coding based on matching pursuits , 1997, IEEE Trans. Circuits Syst. Video Technol..

[24]  Hyun Wook Park,et al.  L=M -Fold Image Resizing in Block-DCT Domain Using Symmetric Convolution , 2001 .

[25]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[26]  Eric Dubois,et al.  Image up-sampling using total-variation regularization with a new observation model , 2005, IEEE Transactions on Image Processing.

[27]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[28]  Hyun Wook Park,et al.  A Ringing-Artifact Reduction Method for Block-DCT-Based Image Resizing , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[29]  Kuo-Liang Chung,et al.  New joint demosaicing and arbitrary-ratio resizing algorithm for color filter array based on DCT approach , 2010, IEEE Transactions on Consumer Electronics.

[30]  Zhenyu Wu,et al.  A New Hybrid DCT-Wiener-Based Interpolation Scheme for Video Intra Frame Up-Sampling , 2010, IEEE Signal Processing Letters.

[31]  Moncef Gabbouj,et al.  Sparse/DCT (S/DCT) Two-Layered Representation of Prediction Residuals for Video Coding , 2013, IEEE Transactions on Image Processing.

[32]  Michael Elad,et al.  Submitted to Ieee Transactions on Image Processing Image Decomposition via the Combination of Sparse Representations and a Variational Approach , 2022 .

[33]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[34]  Rajeev Kumar,et al.  A Fast Arbitrary Factor Video Resizing Algorithm , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[35]  Lawrence Carin,et al.  Exploiting Structure in Wavelet-Based Bayesian Compressive Sensing , 2009, IEEE Transactions on Signal Processing.

[36]  Truong Q. Nguyen,et al.  An Adaptable $k$ -Nearest Neighbors Algorithm for MMSE Image Interpolation , 2009, IEEE Transactions on Image Processing.

[37]  Young-June Choi,et al.  Sparse Signal Recovery by Stepwise Subspace Pursuit in Compressed Sensing , 2013, Int. J. Distributed Sens. Networks.

[38]  Xiaoyan Sun,et al.  Spatially Scalable Video Coding for HEVC , 2012, 2012 IEEE International Conference on Multimedia and Expo.

[39]  Narendra Ahuja,et al.  A fast scheme for image size change in the compressed domain , 2001, IEEE Trans. Circuits Syst. Video Technol..

[40]  Johnson I. Agbinya Interpolation using the discrete cosine transform , 1992 .

[41]  W. Siu,et al.  Fast image interpolation using the bilateral filter , 2012 .

[42]  Truong Q. Nguyen,et al.  Image Superresolution Using Support Vector Regression , 2007, IEEE Transactions on Image Processing.

[43]  Lei Zhang,et al.  Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization , 2010, IEEE Transactions on Image Processing.

[44]  Jhing-Fa Wang,et al.  Classified Multifilter Up-Sampling Algorithm in Spatial Scalability for H.264/SVC Encoder , 2010, IEEE Transactions on Circuits and Systems for Video Technology.