A learned pixel-by-pixel lossless image compression method with 59K parameters and parallel decoding

This paper considers lossless image compression and presents a learned compression system that can achieve state-of-the-art lossless compression performance but uses only 59K parameters, which is one or two order of magnitudes less than other learned systems proposed recently in the literature. The...

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Vydáno v:Multimedia tools and applications Ročník 83; číslo 8; s. 22975 - 22993
Hlavní autoři: Gümüş, Sinem, Kamisli, Fatih
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.03.2024
Springer Nature B.V
Témata:
Algorithms
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Encoding-Decoding
Entropy
Image coding
Image compression
Methods
Multimedia
Multimedia Information Systems
Neural networks
Parameters
Pixels
Probability distribution
Software
Special Purpose and Application-Based Systems
Wavelet transforms
Image compression
Entropy coding
Gaussian mixture model
Artificial neural networks
ISSN:1573-7721, 1380-7501, 1573-7721
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Shrnutí:This paper considers lossless image compression and presents a learned compression system that can achieve state-of-the-art lossless compression performance but uses only 59K parameters, which is one or two order of magnitudes less than other learned systems proposed recently in the literature. The explored system is based on a learned pixel-by-pixel lossless image compression method, where each pixel’s probability distribution parameters are obtained by processing the pixel’s causal neighborhood (i.e. previously encoded/decoded pixels) with a simple neural network comprising 59K parameters. This causality causes the decoder to operate sequentially, i.e. the neural network has to be evaluated for each pixel sequentially, which increases decoding time significantly with common GPU software and hardware. To reduce the decoding time, parallel decoding algorithms are proposed and implemented. The obtained lossless image compression system is compared to traditional and learned systems in the literature in terms of compression performance, encoding-decoding times and computational complexity.
Bibliografie:ObjectType-Article-1
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ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-023-16270-4