Deep Lossy Plus Residual Coding for Lossless and Near-Lossless Image Compression

Lossless and near-lossless image compression is of paramount importance to professional users in many technical fields, such as medicine, remote sensing, precision engineering and scientific research. But despite rapidly growing research interests in learning-based image compression, no published me...

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Vydáno v:IEEE transactions on pattern analysis and machine intelligence Ročník 46; číslo 5; s. 3577 - 3594
Hlavní autoři: Bai, Yuanchao, Liu, Xianming, Wang, Kai, Ji, Xiangyang, Wu, Xiaolin, Gao, Wen
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.05.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Autoregressive models
Codecs
Coding
Context
Data models
Deep learning
Entropy
Entropy coding
Image coding
Image compression
lossless compression
lossy plus residual coding
near-lossless compression
Network architecture
Parallel processing
Remote sensing
Training
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Shrnutí:Lossless and near-lossless image compression is of paramount importance to professional users in many technical fields, such as medicine, remote sensing, precision engineering and scientific research. But despite rapidly growing research interests in learning-based image compression, no published method offers both lossless and near-lossless modes. In this paper, we propose a unified and powerful deep lossy plus residual (DLPR) coding framework for both lossless and near-lossless image compression. In the lossless mode, the DLPR coding system first performs lossy compression and then lossless coding of residuals. We solve the joint lossy and residual compression problem in the approach of VAEs, and add autoregressive context modeling of the residuals to enhance lossless compression performance. In the near-lossless mode, we quantize the original residuals to satisfy a given ℓ ∞ error bound, and propose a scalable near-lossless compression scheme that works for variable ℓ ∞ bounds instead of training multiple networks. To expedite the DLPR coding, we increase the degree of algorithm parallelization by a novel design of coding context, and accelerate the entropy coding with adaptive residual interval. Experimental results demonstrate that the DLPR coding system achieves both the state-of-the-art lossless and near-lossless image compression performance with competitive coding speed.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2023.3348486