@InProceedings{6952027,
  author = {A. Kadaikar and A. Mokraoui and G. Dauphin},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Entropy-constrained dense disparity map estimation algorithm for stereoscopic images},
  year = {2014},
  pages = {241-245},
  abstract = {This paper deals with the stereo matching problem to estimate a dense disparity map. Traditionally a matching metric such as mean square error distortion is adopted to select the best matches associated with disparities. However several disparities related to a given pixel may satisfy the distortion criterion although quite often the choice that is made does not necessarily meet the coding objective. An entropy-constrained disparity optimization approach is developed where the traditional matching metric is replaced by a joint entropy-distortion metric so that the selected disparities reduce not only the reconstructed image distortion but also the entropy disparity. The algorithm sequentially builds a tree avoiding a full search and ensuring good rate-distortion performance. At each tree depth, the M-best retained paths are extended to build new paths to which are assigned entropy-distortion metrics. Simulations show that our algorithm provides better results than dynamic programming algorithm.},
  keywords = {entropy;image matching;image reconstruction;mean square error methods;rate distortion theory;stereo image processing;M-best retained paths;rate-distortion performance;reconstructed image distortion;matching metric;entropy-constrained disparity optimization;distortion criterion;mean square error distortion;stereo matching problem;entropy-constrained dense disparity map estimation;stereoscopic images;Stereoscopic images;matching;disparity;entropy;optimization},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569925545.pdf},
}

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However several disparities related to a given pixel may satisfy the distortion criterion although quite often the choice that is made does not necessarily meet the coding objective. An entropy-constrained disparity optimization approach is developed where the traditional matching metric is replaced by a joint entropy-distortion metric so that the selected disparities reduce not only the reconstructed image distortion but also the entropy disparity. The algorithm sequentially builds a tree avoiding a full search and ensuring good rate-distortion performance. At each tree depth, the M-best retained paths are extended to build new paths to which are assigned entropy-distortion metrics. 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