On the systematic development of fast fuzzy vector quantization for grayscale image compression

DOI10.1016/j.neunet.2012.09.009zbMath1258.68123DBLPjournals/nn/TsolakisTNR12OpenAlexW2045860588WikidataQ48371001 ScholiaQ48371001MaRDI QIDQ1942725

George E. Tsekouras, Anastasios Rigos, Antonios D. Niros, Dimitrios Tsolakis

Publication date: 13 March 2013

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2012.09.009

zbMATH Keywords

image compressionpattern reductioncodeword reductioncodeword relocationfast fuzzy vector quantization

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Computing methodologies for image processing (68U10) Pattern recognition, speech recognition (68T10)

Related Items (1)

The semiring-theoretic approach to MV-algebras: a survey

Cites Work

• Constrained-storage multistage vector quantization based on genetic algorithms
• An adaptive incremental LBG for vector quantization
• Asymptotically optimal block quantization

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