Entropy-constrained geometric vector quantization for transform image coding
- 1 January 1991
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 2269-2272 vol.4
- https://doi.org/10.1109/icassp.1991.150740
Abstract
A noiseless code is combined with a lattice-based vector quantizer (VQ). For small distortion encoding of Laplacian data, the noiseless code has redundancy of at most 2/L, where L is the vector dimension. The VQ and noiseless code are used in discrete cosine transform image coding. An image coder using a single VQ/noiseless code yields performance roughly equivalent to a benchmark coder using entropy-constrained scalar quantization with entropy codes designed for each transform coefficient. The use of several VQ/noiseless codes can further reduce the encoding rate.<>Keywords
This publication has 8 references indexed in Scilit:
- Trellis coded quantization of memoryless and Gauss-Markov sourcesIEEE Transactions on Communications, 1990
- Geometric source coding and vector quantizationIEEE Transactions on Information Theory, 1989
- Scene Adaptive CoderIEEE Transactions on Communications, 1984
- Distributions of the Two-Dimensional DCT Coefficients for ImagesIEEE Transactions on Communications, 1983
- Fast quantizing and decoding and algorithms for lattice quantizers and codesIEEE Transactions on Information Theory, 1982
- Voronoi regions of lattices, second moments of polytopes, and quantizationIEEE Transactions on Information Theory, 1982
- Arithmetic CodingIBM Journal of Research and Development, 1979
- Asymptotically efficient quantizingIEEE Transactions on Information Theory, 1968