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Image Quality Assessment — A Multiscale Geometric Analysis-Based Framework and Examples

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Handbook of Natural Computing

Abstract

This chapter is about objective image quality assessment (IQA), which has been recognized as an effective and efficient way to predict the visual quality of distorted images. Basically, IQA has three different dependent degrees on original images, namely, full-reference (FR), no-reference (NR), and reduced-reference (RR). To begin with, we introduce the fundamentals of IQA and give a broad treatment of the state-of-the-art. We focus on a novel framework for IQA to mimic the human visual system (HVS) by incorporating the merits from multiscale geometric analysis (MGA), contrast sensitivity function (CSF), and Weber's law of just noticeable difference (JND). Thorough empirical studies were carried out using the laboratory for image and video engineering (LIVE) database against subjective mean opinion score (MOS), and these demonstrate that the proposed framework has good consistency with subjective perception values and the objective assessment results well reflect the visual quality of the images.

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Acknowledgments

This research was supported by the National Natural Science Foundation of China (60771068, 60702061, 60832005), the Ph.D. Programs Foundation of the Ministry of Education of China (No. 20090203110002), the Natural Science Basic Research Plan in the Shaanxi Province of China (2009JM8004), the Open Project Program of the National Laboratory of Pattern Recognition (NLPR) in China, and the National Laboratory of Automatic Target Recognition, Shenzhen University, China.

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Authors and Affiliations

  1. Video and Image Processing System Lab, School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, China

    Xinbo Gao

  2. Video and Image Processing System Lab, School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, China

    Wen Lu

  3. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Dacheng Tao

  4. Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, Shaanxi, China

    Xuelong Li  

Authors
  1. Xinbo Gao
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  2. Wen Lu
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  3. Dacheng Tao
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  4. Xuelong Li  
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Editor information

Editors and Affiliations

  1. LIACS, Leiden University, Leiden, The Netherlands

    Grzegorz Rozenberg

  2. Computer Science Department, University of Colorado, Boulder, USA

    Grzegorz Rozenberg

  3. LIACS, Leiden University, Leiden, The Netherlands

    Thomas Bäck

  4. LIACS, Leiden University, Leiden, The Netherlands

    Joost N. Kok

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© 2012 Springer-Verlag Berlin Heidelberg

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Gao, X., Lu, W., Tao, D., Li  , X. (2012). Image Quality Assessment — A Multiscale Geometric Analysis-Based Framework and Examples. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds) Handbook of Natural Computing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92910-9_11

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