A Progressive Universal Noiseless Coder
The authors combine pruned tree-structured vector quantization (pruned TSVQ) with Itoh's (1987) universal noiseless coder. By combining pruned TSVQ with universal noiseless coding, they benefit from the "successive approximation" capabilities of TSVQ, thereby allowing progressive transmission of images, while retaining the ability to noiselessly encode images of unknown statistics in a provably asymptotically optimal fashion. Noiseless compression results are comparable to Ziv-Lempel and arithmetic coding for both images and finely quantized Gaussian sources.
© Copyright 1994 IEEE. Reprinted with permission. Manuscript received November 10, 1992; revised May 18, 1993. This paper is based upon work supported in part by the National Science Foundation under an NSF Graduate Fellowship and NSF Grants MIP-9016974-A1 and MIP-9110508. This paper was presented in part at the IEEE International Symposium on Information Theory, Budapest, Hungary, June 1991. The authors wish to thank Prof. S. Itoh for his helpful comments during the preparation of this paper.