Fast matching pursuit method using property of symmetry and classification for scalable video coding

Matching pursuit is a signal expansion technique whose efficiency for motion compensated residual image has been successfully demonstrated in the MPEG-4 development. However, one of the practical concerns related to applying matching pursuit algorithm to real-time coding of video is its massive computation required for finding atoms. This paper proposes a new fast method based on three properties of basis functions used in the signal expansion. The first one is the symmetry property of the 1-D bases. The second one is that one can preclude many bases that cannot be atom by checking a simple mathematical condition. The last one is the classification property of 2-D bases in a given dictionary. Experimental result shows that our method can perform the same matching pursuit without any image degradation using only about 40% of computational load required by the conventional fast method based on separability of 2-D Gabor dictionary. Furthermore, if negligible quality degradation is allowed, the method can be extended to perform matching pursuit with only about 10% of the computational load required by the conventional fast method. We apply the proposed fast matching pursuit method to scalable coding of video with two layers.