Fast matching pursuit with vector norm comparison

Matching pursuit was demonstrated to be useful especially in low-bit-rate video coding. However, the massive computation required for finding atoms hinders its use in practical applications. This paper provides a new method that can drastically reduce the computational load without any degradation in performance of matching pursuit. We compare vector norms based on the Schwarz inequality to preclude substantial number of dictionary functions without actually evaluating their inner products in atom search. Experimental results show that the number of inner product calculations is only about 20%-35% of the conventional separability-based fast methods.