FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908407127080960 |
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| author | Wang, Zhaoyang Li, Jie Lu, Wen He, Lihuo Gong, Maoguo Gao, Xinbo |
| author_facet | Wang, Zhaoyang Li, Jie Lu, Wen He, Lihuo Gong, Maoguo Gao, Xinbo |
| contents | State-of-the-art (SOTA) compressed video super-resolution (CVSR) models face persistent challenges, including prolonged inference time, complex training pipelines, and reliance on auxiliary information. As video frame rates continue to increase, the diminishing inter-frame differences further expose the limitations of traditional frame-to-frame information exploitation methods, which are inadequate for addressing current video super-resolution (VSR) demands. To overcome these challenges, we propose an efficient and scalable solution inspired by the structural and statistical similarities between hyperspectral images (HSI) and video data. Our approach introduces a compression-driven dimensionality reduction strategy that reduces computational complexity, accelerates inference, and enhances the extraction of temporal information across frames. The proposed modular architecture is designed for seamless integration with existing VSR frameworks, ensuring strong adaptability and transferability across diverse applications. Experimental results demonstrate that our method achieves performance on par with, or surpassing, the current SOTA models, while significantly reducing inference time. By addressing key bottlenecks in CVSR, our work offers a practical and efficient pathway for advancing VSR technology. Our code will be publicly available at https://github.com/handsomewzy/FCA2. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_11545 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution Wang, Zhaoyang Li, Jie Lu, Wen He, Lihuo Gong, Maoguo Gao, Xinbo Image and Video Processing Computer Vision and Pattern Recognition State-of-the-art (SOTA) compressed video super-resolution (CVSR) models face persistent challenges, including prolonged inference time, complex training pipelines, and reliance on auxiliary information. As video frame rates continue to increase, the diminishing inter-frame differences further expose the limitations of traditional frame-to-frame information exploitation methods, which are inadequate for addressing current video super-resolution (VSR) demands. To overcome these challenges, we propose an efficient and scalable solution inspired by the structural and statistical similarities between hyperspectral images (HSI) and video data. Our approach introduces a compression-driven dimensionality reduction strategy that reduces computational complexity, accelerates inference, and enhances the extraction of temporal information across frames. The proposed modular architecture is designed for seamless integration with existing VSR frameworks, ensuring strong adaptability and transferability across diverse applications. Experimental results demonstrate that our method achieves performance on par with, or surpassing, the current SOTA models, while significantly reducing inference time. By addressing key bottlenecks in CVSR, our work offers a practical and efficient pathway for advancing VSR technology. Our code will be publicly available at https://github.com/handsomewzy/FCA2. |
| title | FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.11545 |