Jpeg stereo image lossy recompression with mutual information enhancement

Despite JPEG remaining the most prevalent image compression algorithm, the majority of these algorithms primarily concentrate on uncompressed images, thereby overlooking the substantial quantity of existing compressed JPEG images. The recent strides in JPEG recompression strive to further minimize t...

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Vydané v:Multimedia systems Ročník 31; číslo 5; s. 373
Hlavní autori: Zhou, Junwei, Zhang, Benyi, Wu, Shengping, Zhou, Lei, Yang, Yanchao, Xiang, Jianwen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2025
Springer Nature B.V
Predmet:
Algorithms
Alignment
Cameras
Compressing
Computer Communication Networks
Computer Graphics
Computer Science
Cryptology
Data integration
Data Storage Representation
Discrete cosine transform
Efficiency
Entropy
Image coding
Image compression
Modules
Multimedia Information Systems
Operating Systems
Optimization
Regular Paper
Signal to noise ratio
Distributed coding
Mutual information enhancement
JPEG image lossy recompression
ISSN:0942-4962, 1432-1882
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Shrnutí:Despite JPEG remaining the most prevalent image compression algorithm, the majority of these algorithms primarily concentrate on uncompressed images, thereby overlooking the substantial quantity of existing compressed JPEG images. The recent strides in JPEG recompression strive to further minimize these JPEG file sizes. Unfortunately, the prevalent techniques, especially lossy recompression, frequently fail to fully utilize the correlation of coefficients, resulting in less-than-optimal compression. Moreover, they do not tap into the similarities among JPEG stereo images to boost compression. This paper proposes a novel approach, Distributed Coding for JPEG Lossy Recompression (DCLRC), to enhance the efficiency of JPEG recompression. DCLRC employs an encoding network to eliminate redundant data within the Discrete Cosine Transform (DCT) domain. This is followed by preprocessing via DCT coefficient reconstruction and alignment networks allocated for dimensionality reduction and channel alignment. More significantly, DCLRC capitalizes on the visually similar images in the DCT domain by incorporating a mutual information enhancement module. This pioneering module combines feature extraction, multi-head cross-attention, and information fusion. The experimental results show that DCLRC outperforms existing JPEG lossy recompression techniques, including ULIC, Balle2018, BPG, VCC-Intra, HEVC-Intra, JPEG2000, EILC2022, DSIN, and NDIC2022. DCLRC has achieved an average Peak Signal-to-Noise Ratio (PSNR) improvement of 2.0 dB as well as an average Multi-Scale Structural SIMilarity (MS-SSIM) improvement of 2.3 dB on the KITTI dataset at low bitrates. Such results unequivocally verify the effectiveness of DCLRC in augmenting JPEG recompression performance. Our code is available at https://github.com/wsp11/DCLRC.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0942-4962
1432-1882
DOI:10.1007/s00530-025-01945-9