Image compression with learned lifting-based DWT and learned tree-based entropy models

This paper explores learned image compression based on traditional and learned discrete wavelet transform (DWT) architectures and learned entropy models for coding DWT subband coefficients. A learned DWT is obtained through the lifting scheme with learned nonlinear predict and update filters. Severa...

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Bibliographic Details
Published in:Multimedia systems 2023-12, Vol.29 (6), p.3369-3384
Main Authors: Sahin, Ugur Berk, Kamisli, Fatih
Format: Article
Language:English
Subjects:
Algorithms
Computer Communication Networks
Computer Graphics
Computer Science
Cryptology
Data Storage Representation
Decoding
Discrete Wavelet Transform
Electromagnetic wave filters
Entropy
Image compression
Multimedia Information Systems
Operating Systems
Regular Paper
Wavelet transforms
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Summary:This paper explores learned image compression based on traditional and learned discrete wavelet transform (DWT) architectures and learned entropy models for coding DWT subband coefficients. A learned DWT is obtained through the lifting scheme with learned nonlinear predict and update filters. Several learned entropy models, with varying computational complexities, are explored to exploit inter- and intra-DWT subband coefficient dependencies, akin to traditional EZW, SPIHT, or EBCOT algorithms. Experimental results show that when the explored learned entropy models are combined with traditional wavelet filters, such as the CDF 9/7 filters, compression performance that far exceeds that of JPEG2000 can be achieved. When the learned entropy models are combined with the learned DWT, compression performance increases further. The computations in the learned DWT and all entropy models, except one, can be simply parallelized, and thus, the systems provide practical encoding and decoding times on GPUs, unlike other DWT-based learned compression systems in the literature.
ISSN:0942-4962
1432-1882
DOI:10.1007/s00530-023-01192-w