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| Perceptual image representations in Suppot Vector Machine image coding | |||
| J. Gutiérrez, G. Gomez, G.
Camps and J. Malo Book chapter in: Kernel methods in bioengineering, communications and image processing. Publisher Idea Group Inc. Chapter XIII, pp 298-319 (2007) |
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| Abstract Support Vector Machine image coding relies on the ability of SVMs for function approximation. The size and the profile of the epsilon-insensitivity zone of the Support Vector Regressor (SVR) at some specific image representation determines (1) the amount of selected support vectors (the compression ratio), and (2) the nature of the introduced error (the compression distortion). However, the selection of an appropriate image representation is a key issue for a meaningful design of the epsilon-insensitivity profile. For example, in image coding applications taking human perception into account is of paramount relevance to obtain a good rate-distortion performance. However, depending on the accuracy of the considered perception model, certain image representations are not suitable for SVR training. In this chapter, we analyze the general procedure to take human vision models into account in SVR-based image coding. Specifically, we derive the condition for image representation selection and the associated epsilon-insensitivity profiles. |
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| Keywords: Support Vector Machine, Non-linear Perception Models, Image Coding, Perceptual Metric, Maximum Perceptual Error. References: 32 |
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