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Optimizing Image Quality

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Handbook of Convex Optimization Methods in Imaging Science

Abstract

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.

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Authors and Affiliations

  1. University of Waterloo, Waterloo, ON, N2L 3G1, Canada

    Dominique Brunet, Zhou Wang & Edward R. Vrscay

  2. Indian Institute of Technology Hyderabad, Sangareddy, Khandi, Telangana, India

    Sumohana S. Channappayya

  3. The University of Texas at Austin, Austin, TX, 78712, USA

    Alan C. Bovik

Authors
  1. Dominique Brunet
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  2. Sumohana S. Channappayya
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  3. Zhou Wang
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  4. Edward R. Vrscay
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  5. Alan C. Bovik
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Corresponding author

Correspondence to Zhou Wang .

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Editors and Affiliations

  1. The Pennsylvania State University, University Park, Pennsylvania, USA

    Vishal Monga

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Brunet, D., Channappayya, S.S., Wang, Z., Vrscay, E.R., Bovik, A.C. (2018). Optimizing Image Quality. In: Monga, V. (eds) Handbook of Convex Optimization Methods in Imaging Science. Springer, Cham. https://doi.org/10.1007/978-3-319-61609-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-61609-4_2

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