Conditional entropy-constrained trellis-coded RVQ with application to image coding

Abstract

This paper introduces an extension of conditional entropy-constrained RVQ (CEC-RVQ) that embodies trellis-coded quantization. The method, which we call conditional entropy-constrained trellis-coded residual vector quantization (CEC-TCRVQ), quantizes a supervector (made from a large number of neighboring vectors) to better extract the two-dimensional (2-D) correlation present in real images. Simulation results indicate that CEC-TCRVQ provides 0.3-0.4 dB improvement over CEC-RVQ for the 4/spl times/4 vector case and 1.3 dB improvement for the 8/spl times/8 case.