Conditional entropy-constrained trellis-coded RVQ with applicationto image coding

摘要

This paper introduces an extension of conditionalnentropy-constrained RVQ (CEC-RVQ) that embodies trellis-codednquantization. The method, which we call conditional entropy-constrainedntrellis-coded residual vector quantization (CEC-TCRVQ), quantizes ansupervector (made from a large number of neighboring vectors) to betternextract the two-dimensional (2-D) correlation present in real images.nSimulation results indicate that CEC-TCRVQ provides 0.3-0.4 dBnimprovement over CEC-RVQ for the 4×4 vector case and 1.3 dBnimprovement for the 8×8 case