Optimizing Image Quality

The fact that multimedia services have become the major driver for next generation wireless networks underscores their technological and economic impact. A vast majority of these multimedia services are consumer-centric and therefore must guarantee a certain level of perceptual quality. Given the massive volumes of image and video data in question, it is only natural to adopt automatic quality prediction and optimization tools. The past decade has seen the invention of several excellent automatic quality prediction tools for natural images and videos. While these tools predict perceptual quality scores accurately, they do not necessarily lend themselves to standard optimization techniques. In this chapter, a systematic framework for optimization with respect to a perceptual quality assessment algorithm is presented. The Structural SIMilarity (SSIM) index, which has found vast commercial acceptance owing to its high performance and low complexity, is the representative image quality assessment model that is studied. Specifically, a detailed exposition of the mathematical properties of the SSIM index is presented first, followed by a discussion on the design of linear and non-linear SSIM-optimal image restoration algorithms.

[1]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[2]  Zhou Wang,et al.  Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[3]  Dominique Brunet,et al.  A Study of the Structural Similarity Image Quality Measure with Applications to Image Processing , 2012 .

[4]  Zhou Wang,et al.  Video quality assessment based on structural distortion measurement , 2004, Signal Process. Image Commun..

[5]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[6]  B. R. Hunt,et al.  Digital Image Restoration , 1977 .

[7]  Nikolay N. Ponomarenko,et al.  TID2008 – A database for evaluation of full-reference visual quality assessment metrics , 2004 .

[8]  Zhou Wang,et al.  On the Mathematical Properties of the Structural Similarity Index , 2012, IEEE Transactions on Image Processing.

[9]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[10]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[11]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[12]  Edward R. Vrscay,et al.  Unconstrained Structural Similarity-Based Optimization , 2014, ICIAR.

[13]  Edward R. Vrscay,et al.  SSIM-inspired image denoising using sparse representations , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  Marco Carli,et al.  Modified image visual quality metrics for contrast change and mean shift accounting , 2011, 2011 11th International Conference The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM).

[15]  Zhou Wang,et al.  Reduced- and No-Reference Image Quality Assessment , 2011, IEEE Signal Processing Magazine.

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

[17]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[18]  Edward R. Vrscay,et al.  Structural Similarity-Based Optimization Problems with L^1 -Regularization: Smoothing Using Mollifiers , 2015, ICIAR.

[19]  David J. Sakrison,et al.  The effects of a visual fidelity criterion of the encoding of images , 1974, IEEE Trans. Inf. Theory.

[20]  Zhou Wang,et al.  Complex Wavelet Structural Similarity: A New Image Similarity Index , 2009, IEEE Transactions on Image Processing.

[21]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[22]  Nikolay N. Ponomarenko,et al.  Image database TID2013: Peculiarities, results and perspectives , 2015, Signal Process. Image Commun..

[23]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[24]  Aggelos K. Katsaggelos,et al.  Digital image restoration , 2012, IEEE Signal Process. Mag..

[25]  Wei Zhang,et al.  The Application of Visual Saliency Models in Objective Image Quality Assessment: A Statistical Evaluation , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[26]  Zhou Wang,et al.  Information Content Weighting for Perceptual Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[27]  Hua Yang,et al.  Sparse Feature Fidelity for Perceptual Image Quality Assessment , 2013, IEEE Transactions on Image Processing.

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

[29]  Edward R. Vrscay,et al.  SSIM-inspired image restoration using sparse representation , 2012, EURASIP Journal on Advances in Signal Processing.

[30]  Edward R. Vrscay,et al.  Solving Optimization Problems That Employ Structural Similarity As The Fidelity Measure , 2014 .

[31]  Michael Elad,et al.  Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit , 2008 .

[32]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[33]  Krzysztof Okarma.  Colour Image Quality Assessment Using Structural Similarity Index and Singular Value Decomposition , 2008, ICCVG.

[34]  Robert W. Heath,et al.  Perceptual soft thresholding using the structural similarity index , 2008, 2008 15th IEEE International Conference on Image Processing.

[35]  Eero P. Simoncelli,et al.  Image restoration using Gaussian scale mixtures in the wavelet domain , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[36]  Orly Yadid-Pecht,et al.  Quaternion Structural Similarity: A New Quality Index for Color Images , 2012, IEEE Transactions on Image Processing.

[37]  Hongyu Li,et al.  SR-SIM: A fast and high performance IQA index based on spectral residual , 2012, 2012 19th IEEE International Conference on Image Processing.

[38]  Zhou Wang,et al.  Structural Similarity-Based Approximation of Signals and Images Using Orthogonal Bases , 2010, ICIAR.

[39]  V. K. Bairagi,et al.  A no-reference image quality assessment , 2013, 2013 IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN).

[40]  S. Mallat,et al.  Adaptive greedy approximations , 1997 .

[41]  Zhou Wang,et al.  Spatial Pooling Strategies for Perceptual Image Quality Assessment , 2006, 2006 International Conference on Image Processing.

[42]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[43]  Robert W. Heath,et al.  A Linear Estimator Optimized for the Structural Similarity Index and its Application to Image Denoising , 2006, 2006 International Conference on Image Processing.

[44]  C.-C. Jay Kuo,et al.  Perceptual image quality assessment using block-based multi-metric fusion (BMMF) , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[45]  I. Johnstone,et al.  Adapting to Unknown Smoothness via Wavelet Shrinkage , 1995 .