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A Spatial Regularization Approach for Vector Quantization

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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.

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Authors and Affiliations

  1. Lab. Informatique Gaspard Monge, UMR CNRS 8049, Université Paris-Est, Champs-sur-Marne, 77454, Marne-la-Vallée, France

    Caroline Chaux, Anna Jezierska, Jean-Christophe Pesquet & Hugues Talbot

Authors
  1. Caroline Chaux
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  2. Anna Jezierska
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  3. Jean-Christophe Pesquet
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  4. Hugues Talbot
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Corresponding author

Correspondence to Anna Jezierska.

Additional information

This work was supported by the Agence Nationale de la Recherche under grant ANR-09-EMER-004-03.

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Chaux, C., Jezierska, A., Pesquet, JC. et al. A Spatial Regularization Approach for Vector Quantization. J Math Imaging Vis 41, 23–38 (2011). https://doi.org/10.1007/s10851-010-0241-3

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