End-to-End RGB-IR Joint Image Compression With Channel-wise Cross-modality Entropy Model
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909661906599936 |
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| author | Wang, Haofeng Zhou, Fangtao Zhang, Qi Chen, Zeyuan Zhang, Enci Wang, Zhao Huang, Xiaofeng Ma, Siwei |
| author_facet | Wang, Haofeng Zhou, Fangtao Zhang, Qi Chen, Zeyuan Zhang, Enci Wang, Zhao Huang, Xiaofeng Ma, Siwei |
| contents | RGB-IR(RGB-Infrared) image pairs are frequently applied simultaneously in various applications like intelligent surveillance. However, as the number of modalities increases, the required data storage and transmission costs also double. Therefore, efficient RGB-IR data compression is essential. This work proposes a joint compression framework for RGB-IR image pair. Specifically, to fully utilize cross-modality prior information for accurate context probability modeling within and between modalities, we propose a Channel-wise Cross-modality Entropy Model (CCEM). Among CCEM, a Low-frequency Context Extraction Block (LCEB) and a Low-frequency Context Fusion Block (LCFB) are designed for extracting and aggregating the global low-frequency information from both modalities, which assist the model in predicting entropy parameters more accurately. Experimental results demonstrate that our approach outperforms existing RGB-IR image pair and single-modality compression methods on LLVIP and KAIST datasets. For instance, the proposed framework achieves a 23.1% bit rate saving on LLVIP dataset compared to the state-of-the-art RGB-IR image codec presented at CVPR 2022. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21851 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | End-to-End RGB-IR Joint Image Compression With Channel-wise Cross-modality Entropy Model Wang, Haofeng Zhou, Fangtao Zhang, Qi Chen, Zeyuan Zhang, Enci Wang, Zhao Huang, Xiaofeng Ma, Siwei Computer Vision and Pattern Recognition Multimedia Image and Video Processing RGB-IR(RGB-Infrared) image pairs are frequently applied simultaneously in various applications like intelligent surveillance. However, as the number of modalities increases, the required data storage and transmission costs also double. Therefore, efficient RGB-IR data compression is essential. This work proposes a joint compression framework for RGB-IR image pair. Specifically, to fully utilize cross-modality prior information for accurate context probability modeling within and between modalities, we propose a Channel-wise Cross-modality Entropy Model (CCEM). Among CCEM, a Low-frequency Context Extraction Block (LCEB) and a Low-frequency Context Fusion Block (LCFB) are designed for extracting and aggregating the global low-frequency information from both modalities, which assist the model in predicting entropy parameters more accurately. Experimental results demonstrate that our approach outperforms existing RGB-IR image pair and single-modality compression methods on LLVIP and KAIST datasets. For instance, the proposed framework achieves a 23.1% bit rate saving on LLVIP dataset compared to the state-of-the-art RGB-IR image codec presented at CVPR 2022. |
| title | End-to-End RGB-IR Joint Image Compression With Channel-wise Cross-modality Entropy Model |
| topic | Computer Vision and Pattern Recognition Multimedia Image and Video Processing |
| url | https://arxiv.org/abs/2506.21851 |