Image coding using entropy-constrained reflected residual vector quantization

Residual vector quantization (RVQ) is a structurally constrained vector quantization (VQ) paradigm. RVQ employs multipath search and has higher encoding cost as compared to sequential single-path search. Reflected residual vector quantization (Ref-RVQ) design with additional symmetry on the codebook was developed later to jointly optimized RVQ structure with single-path search. The constrained Ref-RVQ codebook exhibits an increase in distortion. However, it was conjectured that Ref-RVQ codebook has a lower output entropy than that of multipath RVQ codebook. Therefore, Ref-RVQ design was generalized to include noiseless entropy coding. In this paper, we apply it to image coding. The method is referred to as Entropy-constrained Ref-RVQ (EC-Ref-RVQ). Since RVQ scheme is able to implement very large dimensional vector quantization designs like 16 × 16 and 32 × 32 VQ's, it is found highly successful in extracting linear and non-linear correlation among the image pixels. We intend to implement these large dimensional vectors with EC-Ref-RVQ scheme to realize a computationally less demanding image-RVQ design. Simulation results demonstrate that EC-Ref-RVQ, while maintaining single path search, provides 1 dB improvement in PSNR for image data over the multipath EC-RVQ.