Adaptive-search tree-structured residual vector quantization

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.