8. Application of Attention Models in Image Processing

  1. Liming Zhang1 and
  2. Weisi Lin2

Published Online: 20 MAR 2013

DOI: 10.1002/9780470828144.ch8

Selective Visual Attention: Computational Models and Applications

Selective Visual Attention: Computational Models and Applications

How to Cite

Zhang, L. and Lin, W. (2013) Application of Attention Models in Image Processing, in Selective Visual Attention: Computational Models and Applications, John Wiley & Sons (Asia) Pte Ltd, Singapore. doi: 10.1002/9780470828144.ch8

Author Information

  1. 1

    Fudan University, P. R. China

  2. 2

    Nanyang Technological University, Singapore

Publication History

  1. Published Online: 20 MAR 2013
  2. Published Print: 27 MAR 2013

ISBN Information

Print ISBN: 9780470828120

Online ISBN: 9780470828144

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Keywords:

  • just noticeable difference (JND);
  • quality assessment (QA);
  • structural similarity (SSIM);
  • image and video coding;
  • discrete cosine transform (DCT);
  • region of interest (RoI);
  • quantization parameter (QP);
  • foveation visibility threshold;
  • quality pooling;
  • down-sampling;
  • quantization;
  • image retargeting;
  • compressive sensing(CS);
  • restricted isometry property (RIP) condition

Summary

This chapter introduces applications of visual attention models in image processing related areas: just noticeable difference (JND) modelling, visual quality assessment, image and video coding, visual signal retargeting and compressive sensing.

Section 8.1 illustrates the combination of visual attention and the JND model towards a complete visibility threshold model which can be used in a wide spectrum of uses. The application of attention models in quality assessment (QA) of images and video is presented in Section 8.2, and a typical QA index, SSIM, is weighted by visual attention in different ways. The use of attention models to explore visual redundancy for better image coding is introduced in Section 8.3. The applications for image retargeting and compressive sensing are presented in Sections 8.4 and 8.5, respectively.