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Computer Optics, 2017, Volume 41, Issue 6, Pages 905–912
DOI: https://doi.org/10.18287/2412-6179-2017-41-6-905-912
(Mi co464)
 

IMAGE PROCESSING, PATTERN RECOGNITION

Statistical encoding for image compression based on hierarchical grid interpolation

M. V. Gashnikov

Samara National Research University, Samara, Russia
References:
Abstract: Algorithms of statistical encoding for image compression are investigated. An approach is proposed to increase the efficiency of variable-length codes when compressing images with losses. An algorithm of statistical encoding is developed for use as part of image compression methods that encode a de-correlated signal with an uneven probability distribution. An experimental comparison of the proposed algorithm with the algorithms ZIP and ARJ is performed while encoding the specific data of the hierarchical compression method. In addition, an experimental comparison of the hierarchical method of image compression, including the developed coding algorithm, with the JPEG method and the method based on the wavelet transform is carried out.
Keywords: image compression, statistical encoding, variable length codes, quantization, entropy, compressed data size.
Funding agency Grant number
Russian Science Foundation 14-31-00014
The work was funded by the Russian Science Foundation, grant No. 14-31-00014.
Received: 26.07.2017
Accepted: 23.09.2017
Document Type: Article
Language: Russian
Citation: M. V. Gashnikov, “Statistical encoding for image compression based on hierarchical grid interpolation”, Computer Optics, 41:6 (2017), 905–912
Citation in format AMSBIB
\Bibitem{Gas17}
\by M.~V.~Gashnikov
\paper Statistical encoding for image compression based on hierarchical grid interpolation
\jour Computer Optics
\yr 2017
\vol 41
\issue 6
\pages 905--912
\mathnet{http://mi.mathnet.ru/co464}
\crossref{https://doi.org/10.18287/2412-6179-2017-41-6-905-912}
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