A Spectrally Adaptive Noise Filling Tool for Perceptual Transform Coding of Still Images

Modern perceptual image coders reach impressively high subjective quality even at low bit-rates but tend to denoise or “detexturize” the coded pictures. Traditionally, two independent parametric approaches, known as texture and film grain synthesis, have been applied in the spatial domain as pre and post-processors around the codec to counteract such effects. In this work, a unified alternative, operating directly within the spectral domain of conventional transform codecs with tight coupling to the transform coefficient quantizer, is proposed. Due to its design, this spectrally adaptive noise filling tool (SANFT) enables highly input adaptive realizations by reusing the coder's existing optimized spatial and spectral partitioning algorithms. Formal subjective evaluation in the context of a main still picture” High Emciency Video Coding (HEVC) implementation confirms the benefit of the proposal.

[1]  Dimitrios Hatzinakos,et al.  Film grain noise removal and generation for color images , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[2]  Minhua Zhou,et al.  HEVC Deblocking Filter , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[3]  Yoonsik Choe,et al.  Enhanced Film Grain Noise Removal for High Fidelity Video Coding , 2013, 2013 International Conference on Information Science and Cloud Computing Companion.

[4]  Alessandro Neri,et al.  A perceptually lossless, model-based, texture compression technique , 2000, IEEE Trans. Image Process..

[5]  Gary J. Sullivan,et al.  High Efficiency Video Coding (HEVC), Algorithms and Architectures , 2014, Integrated Circuits and Systems.

[6]  K. R. Rao,et al.  High efficiency video coding , 2016, 2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA).

[7]  Oscar C. Au,et al.  Film grain noise removal and synthesis in video coding , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[8]  C.-C. Jay Kuo,et al.  Synthesis-Based Texture Video Coding With Side Information , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Yongdong Zhang,et al.  High Efficiency Video Coding: High Efficiency Video Coding , 2014 .

[10]  Vincent Ricordel,et al.  Local texture synthesis: A static texture coding algorithm fully compatible with HEVC , 2015, 2015 International Conference on Systems, Signals and Image Processing (IWSSIP).

[11]  Heiko Schwarz,et al.  Improved H.264/AVC coding using texture analysis and synthesis , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[12]  Oscar C. Au,et al.  DCT coefficients generation model for film grain noise and its application in super-resolution , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[13]  Mathias Wien,et al.  High Efficiency Video Coding: Coding Tools and Specification , 2014 .

[14]  Russell M. Mersereau,et al.  Lossy compression of noisy images , 1998, IEEE Trans. Image Process..

[15]  F. Bossen,et al.  Common test conditions and software reference configurations , 2010 .

[16]  S. A. Martucci,et al.  Reversible compression of HDTV images using median adaptive prediction and arithmetic coding , 1990, IEEE International Symposium on Circuits and Systems.

[17]  Gary J. Sullivan,et al.  Efficient scalar quantization of exponential and Laplacian random variables , 1996, IEEE Trans. Inf. Theory.

[18]  Fan Zhang,et al.  Compact Representation for Dynamic Texture Video Coding Using Tensor Method , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[19]  Takehiro Moriya,et al.  Low delay LPC and MDCT-based audio coding in the EVS codec , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[20]  Sugato Chakravarty,et al.  Methodology for the subjective assessment of the quality of television pictures , 1995 .

[21]  Kemal Ugur,et al.  Intra Coding of the HEVC Standard , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[22]  ITU-T Rec. P.910 (04/2008) Subjective video quality assessment methods for multimedia applications , 2009 .

[23]  Barry G. Haskell,et al.  An encoder-decoder texture replacement method with application to content-based movie coding , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[24]  Johannes Ballé,et al.  Component-based image coding using non-local means filtering and an autoregressive texture model , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[25]  Andreas Niedermeier,et al.  Low-complexity semi-parametric joint-stereo audio transform coding , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

[26]  Chia-Yang Tsai,et al.  Sample Adaptive Offset in the HEVC Standard , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[27]  C.-C. Jay Kuo,et al.  Film grain noise modeling in advanced video coding , 2007, Electronic Imaging.

[28]  Jens-Rainer Ohm,et al.  Models for Static and Dynamic Texture Synthesis in Image and Video Compression , 2011, IEEE Journal of Selected Topics in Signal Processing.

[29]  Hongyuan Zha,et al.  Manifold Based Dynamic Texture Synthesis from Extremely Few Samples , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  John Vanderkooy,et al.  Quantization and Dither: A Theoretical Survey , 1992 .