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 tran...

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Bibliographic Details
Published inIEEE transactions on information theory Vol. 40; no. 1; pp. 108 - 117
Main Authors Effros, M., Chou, P.A., Riskin, E.A., Gray, R.M.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.01.1994
Institute of Electrical and Electronics Engineers
Subjects
Applied sciences
Biomedical imaging
Bit rate
Coding, codes
Decoding
Exact sciences and technology
Gaussian noise
Image coding
Image databases
Image reconstruction
Image storage
Information, signal and communications theory
Noise robustness
Signal and communications theory
Statistics
Telecommunications and information theory
Arithmetic code
Medical imagery
Image transmission
Vector quantization
Image processing
Coding
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Summary: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.< >
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0018-9448
1557-9654
DOI:10.1109/18.272460