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Journal of Mathematical Imaging and Vision

, Volume 41, Issue 1–2, pp 23–38 | Cite as

A Spatial Regularization Approach for Vector Quantization

  • Caroline Chaux
  • Anna JezierskaEmail author
  • Jean-Christophe Pesquet
  • Hugues Talbot
Article

Abstract

Quantization, defined as the act of attributing a finite number of levels to an image, is an essential task in image acquisition and coding. It is also intricately linked to image analysis tasks, such as denoising and segmentation. In this paper, we investigate vector quantization combined with regularity constraints, a little-studied area which is of interest, in particular, when quantizing in the presence of noise or other acquisition artifacts. We present an optimization approach to the problem involving a novel two-step, iterative, flexible, joint quantizing-regularization method featuring both convex and combinatorial optimization techniques. We show that when using a small number of levels, our approach can yield better quality images in terms of SNR, with lower entropy, than conventional optimal quantization methods.

Keywords

Vector quantization Convex optimization Combinatorial optimization Proximal methods Graph cuts Image coding Compression Information theory Entropy Segmentation Denoising Regularization 

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Caroline Chaux
    • 1
  • Anna Jezierska
    • 1
    Email author
  • Jean-Christophe Pesquet
    • 1
  • Hugues Talbot
    • 1
  1. 1.Lab. Informatique Gaspard Monge, UMR CNRS 8049Université Paris-EstMarne-la-ValléeFrance

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