Joint disparity and variable size-block optimization algorithm for stereoscopic image compression

Abstract This paper addresses the disparity map estimation problem in the context of stereoscopic image coding. It is undeniable that the use of variable size blocks offers the possibility to describe more precisely the predicted view but at the expense of a high bitrate if no particular consideration is taken into account by the estimation algorithm. Indeed more information related to the block layout, considered here as a block-length map, is required at the prediction step. This paper presents an algorithm which jointly optimizes the block-length map as well as the disparity map so as to ensure a good reconstruction of the predicted view while minimizing the bitrate. This is done thanks to a joint metric taking into account the quality of the reconstruction as well as the bitrate needed to encode the maps. Moreover the developed algorithm iteratively improves its performance by refining the estimated maps. Simulation results conducted on several stereoscopic images from the CMU-VASC and the Middlebury dataset confirm the benefits of this approach as compared to competitive block matching algorithms.