Pruned tree-structured vector quantization in image coding

A recently developed technique for variable-rate vector quantizer (VQ) design by P.A. Chou et al. (see IEEE Trans. Inf. Theory, vol.35, no.2, p.299-315, 1989) has been applied to both memoryless and predictive VQ of images. This technique, called pruned tree-structured vector quantization (PTSVQ), uses variable-depth encoders that are tree-structured and thus have very low design and search complexity. PTSVQ is applied to a series of medical images, and gains over full-search VQ of up to 3.78 dB in the signal-to-noise-ratio (SNR) are measured. On still images from the USC database, gains of up to 1.63 dB in the peak SNR are realized for predictive PTSVQ over predictive full search VQ, resulting in high image quality at 0.51 bits per pixel