Trellis-coded residual vector quantization: its geometrical advantages and application to image coding

Residual vector quantization (RVQ), also known as multistage vector quantization, is investigated in the context of quantization cell shapes and is found to produce oblong cell shapes and suboptimal point densities. The oblong cell shapes are partially responsible for the performance degradation of RVQ compared with vector quantization (VQ). In an attempt to realize better cell shapes, a trellis-coded RVQ (TCRVQ) is suggested and is shown to provide optimal point densities and square cell shapes. In order to improve the performance of TCRVQ, an entropy-constrained TCRVQ (EC-TCRVQ) is designed and implemented for non-uniformly distributed sources. The simulation results indicate a performance improvement of 1.5 dB for EC-TCRVQ over entropy-constrained trellis-coded VQ. For an image coding application, we have developed conditional EC-TCRVQ, by employing adjacent vector conditioning in addition to residual vector conditioning. The simulation tests show that the 8×8 CEC-TCRVQ outperforms other predictive and trellis-based VQ schemes