@INPROCEEDINGS{Jezierska2010_ICIP,
  title = {Image quantization under spatial smoothness constraints},
  author = {Jezierska, A. and Chaux, C. and Talbot, H. and Pesquet, J.-C.},
  booktitle = {Image Processing ({ICIP}), 2010 17th {IEEE} International Conference on Image Processing},
  pages = {4297--4300},
  address = {Hong Kong},
  year = {2010},
  month = {Sep.},
  abstract = {Quantization, defined as the act of attributing a finite number of
       grey-levels to an image, is an essential task in image
       acquisition and coding. It is also intricately linked to
       various image analysis tasks, such as denoising and
       segmentation. In this paper, we investigate 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 grey-
       levels, our approach can yield better quality images in terms
       of SNR, with lower entropy, than conventional optimal
       quantization methods.},
  doi = {10.1109/ICIP.2010.5651796},
  ISSN = {1522-4880},
}