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Entropy-constrained dense disparity map estimation algorithm for stereoscopic images

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.