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Why Shape Coding? Asymptotic Analysis of the Entropy Rate for Digital Images.
- Source :
- Entropy; Jan2023, Vol. 25 Issue 1, p48, 10p
- Publication Year :
- 2023
-
Abstract
- This paper focuses on the ultimate limit theory of image compression. It proves that for an image source, there exists a coding method with shapes that can achieve the entropy rate under a certain condition where the shape-pixel ratio in the encoder/decoder is O (1 / log t) . Based on the new finding, an image coding framework with shapes is proposed and proved to be asymptotically optimal for stationary and ergodic processes. Moreover, the condition O (1 / log t) of shape-pixel ratio in the encoder/decoder has been confirmed in the image database MNIST, which illustrates the soft compression with shape coding is a near-optimal scheme for lossless compression of images. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10994300
- Volume :
- 25
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Entropy
- Publication Type :
- Academic Journal
- Accession number :
- 161480035
- Full Text :
- https://doi.org/10.3390/e25010048