Improving Block-Matching Algorithm by Selecting Disparity Sets Minimizing Distortion for Stereoscopic Image Coding

This paper deals with the blockwise disparity map estimation problem for stereoscopic image coding. Generally, disparities are selected amongst a search area by minimizing a local distortion. In addition the larger the search area is, the more often a better disparity can be chosen and the lower the global distortion is. However, the resulting disparity map containing higher number of different disparities is encoded with a larger bitrate. This paper proposes two approaches to take advantage of large search areas while reducing not only the bitrate of the estimated disparity map but also the computational complexity of the optimal solution. The developed sub-optimal algorithms rely on the initial set of disparities selected by the traditional Block-Matching Algorithm (BMA) to compute new sets minimizing the distortion of the predicted view under a bitrate constraint. Simulation results confirm the benefits of our algorithms compared to the BMA in terms of bitrate-distortion.