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A Fast Full-Search Adaptive Vector Quantizer For Video Coding, Scott E. Budge, Christian B. Peel
A Fast Full-Search Adaptive Vector Quantizer For Video Coding, Scott E. Budge, Christian B. Peel
Electrical and Computer Engineering Faculty Publications
This paper presents a novel VQ structure which provides very good quality encoding for video sequences and exploits the computational savings gained from a fast-search algorithm. It uses an adaptive-search, variable-length encoding method which allows for very fast matching of a wide range of transmission rates. Both the encoding quality and the computational benefits from the fast-search algorithm are presented. Simulations show that full-search tree residual VQ (FTRVQ) can provide up to 3 dB improvement over a similar RVQ encoder on video sequences.
Adaptive-Rate Tree-Structured Residual Vector Quantization, Christian B. Peel, X. Liu, Scott E. Budge
Adaptive-Rate Tree-Structured Residual Vector Quantization, Christian B. Peel, X. Liu, Scott E. Budge
Electrical and Computer Engineering Faculty Publications
Full-search vector quantization (VQ) provides optimal results only with high memory and computational cost. We describe the computational and memory requirements of tree-structured VQ, residual VQ (RVQ), and tree-structured RVQ. We present multiple-rate, adaptive-search implementations of these VQ structures, and simulation results with video sequences. Tree-structured RVQ provides up to 1.5 db PSNR quality improvements over RVQ, as well as significant perceptual improvement. These algorithms maintain many of the benefits of full-search VQ, while providing trade-offs between computational, storage, and performance requirements.