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Functional Approximation

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Statistical Learning and Pattern Analysis for Image and Video Processing

Part of the book series: Advances in Pattern Recognition ((ACVPR))

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Abstract

This chapter presents a review of recent advances in the adaptive wavelet transform applied to image and video coding. We focus on research to improve the properties of the wavelet transform rather than on the entire encoder. These advances include enhancements to the construction of an adaptive wavelet transform that results in fewer wavelet coefficients and improvements in motion-compensated temporal filtering that achieve temporal scalability for video compression. These nonlinear wavelet transforms provide added flexibility for image and video representations and accomplish higher compression efficiency than traditional wavelets. The authors also discuss several future research directions in the summary.

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Authors and Affiliations

  1. Inst. Artificial Intelligence & Robotics, Xi’an Jiaotong University, 28 Xianningxilu, 710049, Xi’an, China People’s Republic

    Prof. Nanning Zheng & Assoc.Prof. Jianru Xue

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  1. Prof. Nanning Zheng
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  2. Assoc.Prof. Jianru Xue
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Correspondence to Nanning Zheng .

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© 2009 Springer-Verlag London Limited

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Zheng, N., Xue, J. (2009). Functional Approximation. In: Statistical Learning and Pattern Analysis for Image and Video Processing. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84882-312-9_5

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  • DOI: https://doi.org/10.1007/978-1-84882-312-9_5

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-311-2

  • Online ISBN: 978-1-84882-312-9

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