Title :
Adaptive entropy-coded pruned tree-structured predictive vector quantization of images
Author :
Kim, Yong Han ; Modestino, James W.
Author_Institution :
Dept. of Electr., Comput. & Syst. Eng., Rensselaer Polytech. Inst., Troy, NY, USA
fDate :
1/1/1993 12:00:00 AM
Abstract :
Simpler versions of a previously introduced adaptive entropy-coded predictive vector quantization (PVQ) scheme where the embedded entropy constrained vector quantizer (ECVQ) is replaced by a pruned tree-structured VQ (PTSVQ) are described. The resulting encoding scheme is shown to result in drastically reduced complexity at only a small cost in performance. Coding results for selected real-world images are given
Keywords :
entropy; filtering and prediction theory; image coding; trees (mathematics); vector quantisation; adaptive entropy-coded predictive vector quantization; encoding scheme; image coding; pruned tree-structured predictive vector quantization; Algorithm design and analysis; Buffer storage; Decoding; Feedback loop; Image coding; Image reconstruction; Image storage; Rate-distortion; Robustness; Vector quantization;
Journal_Title :
Communications, IEEE Transactions on
