IMAGE PROCESSING, PATTERN RECOGNITION
Minimizing the entropy of post-interpolation residuals for image compression based on hierarchical grid interpolation
M. V. Gashnikov
Samara National Research University, Samara, Russia
An adaptive parameterized interpolator for image compression based on hierarchical grid interpolation is developed and investigated. For optimizing the interpolator parameters an approach is proposed based on the minimization of the entropy of the quantized post-interpolation residuals, which is used as an estimate of the volume of compressed data. A recursive procedure for calculating the parameters of the developed interpolator is proposed, and theoretical estimates of its computational complexity are calculated. As part of a hierarchical image compression method, the developed interpolator is experimentally investigated, as well as making its comparison with averaging interpolators and an adaptive interpolator based on optimizing the sum of the absolute values of the interpolation errors. The developed interpolator is shown to have an advantage over the prototypes in terms of the compressed data size for various compression errors.
hierarchical grid interpolation, compression, quantization, compression ratio, maximum deviation, computation complexity.
|Russian Science Foundation
|The work was funded by the Russian Science Foundation, grant No. 14-31-00014.
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M. V. Gashnikov, “Minimizing the entropy of post-interpolation residuals for image compression based on hierarchical grid interpolation”, Computer Optics, 41:2 (2017), 266–275
Citation in format AMSBIB
\paper Minimizing the entropy of post-interpolation residuals for image compression based on hierarchical grid interpolation
\jour Computer Optics
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