A 2-D filtering scheme for stereo image compression using sequential orthogonal subspace updating

Abstract

Stereo image compression involves estimating the disparity vectors that represent the amount of binocular parallax. The mismatching problems between the left and right images greatly impact the accuracy of the reconstructed image, and hence the visual effects of the reproduced 3-D image. This paper presents a new method for compensating the mismatching effects in stereo image pairs. This 2-D filtering-based scheme uses a sequential orthogonal subspace updating (SOSU) process to project an image block onto a subset of best-basis vectors. The basis vectors are selected one by one from the neighboring blocks, as well as some typical edge blocks, forming an image-dependent set of basis vectors. This leads to the optimal representation of an image block with fewer coefficients. Simulation results on two different image pairs demonstrate the effectiveness of the SOSU scheme when compared to those of the standard least squares 2-D filtering and the hybrid disparity-compensated discrete cosine transform residual encoding schemes.