A jointly optimized subband coder

The mainstream approach to subband coding has been to partition the input signal into subband signals and to code those signals separately with optimal or near-optimal quantizers and entropy coders. A more effective approach, however, is one where the subband coders are optimized jointly so that the...

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Vydané v:IEEE transactions on image processing Ročník 5; číslo 9; s. 1311 - 1323
Hlavní autori: Kossentini, F., Wilson Ching-Lan Chung, Smith, M.J.T.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.09.1996
Institute of Electrical and Electronics Engineers
Predmet:
Algorithm design and analysis
Applied sciences
Bit rate
Constraint optimization
Design optimization
Entropy
Exact sciences and technology
Frequency
Image coding
Image processing
Information, signal and communications theory
Nonlinear distortion
Rate-distortion
Signal processing
Signal processing algorithms
Telecommunications and information theory
Signal compression
Vector quantization
Image processing
Coding
Coding circuit
Signal processing
Performance
Algorithm
Optimization
ISSN:1057-7149, 1941-0042
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Shrnutí:The mainstream approach to subband coding has been to partition the input signal into subband signals and to code those signals separately with optimal or near-optimal quantizers and entropy coders. A more effective approach, however, is one where the subband coders are optimized jointly so that the average distortion introduced by the subband quantizers is minimized subject to a constraint on the output rate of the subband encoder. A subband coder with jointly optimized multistage residual quantizers and entropy coders is introduced and applied to image coding. The high performance of the coder is attributed to its ability to exploit statistical dependencies within and across the subbands. The efficiency of the multistage residual quantization structure and the effectiveness of the statistical modeling algorithm result in an attractive balance among the reproduction quality, rate, and complexity.
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ISSN:1057-7149
1941-0042
DOI:10.1109/83.535843