Orthogonal subspace projection filtering for stereo image compression

This paper presents a 2-D filtering scheme for stereo image compression using orthogonal subspace projection. To provide more candidate blocks for input data, the support region for input data is extended in the reference image. In addition, edge blocks are added to the candidate input blocks in order to provide better compensation ability for edges and boundaries of objects. The best blocks for input data are selected one by one in order of importance to reconstruct the desired block using the Gram-Schmidt orthogonalization algorithm. Simulation results exhibit excellent performance of the proposed scheme when compared to those of the standard block-matching and least-squares(LS)-based 2-D filtering schemes.