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Compressed video quality enhancement algorithm based on 3D-CNNs

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Abstract

By exploring the current block-based lossy video coding process and compressed videos, this paper finds two unique characteristics namely quality fluctuation and pixel deficiency. And we use 3D convolutional neural network (3D-CNN) to make full use of the limited temporal and spatial information in compressed video and build compressed video quality enhancement network (CVQENet) to improve the compressed video quality. The experimental results show that compared with the videos encoded by High Efficiency Video Coding (HEVC/H.265), the mean value of the Peak Signal-to-Noise Ratio (PSNR) of enhanced videos has been improved by 0.4652 dB under Low Delay (LD) configuration with Quantization Parameter (QP) is set to 37.

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Acknowledgements

This research is supported by The Beijing Natural Science Foundation (Grant No. 4212001) and by Key R &D and Transformation Program of Qinghai Province (Grant No. 2022-QY-205).

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Authors and Affiliations

  1. College of Physics and Electronic Information Engineering, Qinghai Minzu University, Xining, 810000, Qinghai, China

    Shanji Chen & Lingfei Zhang

  2. The Information Department, Beijing University of Technology, Beijing, 100124, China

    Pengyu Liu, Yue Zhang, Sirong Wang & Jing Yuan

  3. Advanced Information Network Beijing Laboratory, Beijing, 100124, China

    Pengyu Liu, Yue Zhang, Sirong Wang & Jing Yuan

  4. Computational Intelligence and Intelligent Systems Beijing Key Laboratory, Beijing, 100124, China

    Pengyu Liu, Yue Zhang, Sirong Wang & Jing Yuan

Authors
  1. Shanji Chen
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  2. Pengyu Liu
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  3. Yue Zhang
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  4. Lingfei Zhang
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  5. Sirong Wang
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  6. Jing Yuan
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Correspondence to Pengyu Liu.

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Chen, S., Liu, P., Zhang, Y. et al. Compressed video quality enhancement algorithm based on 3D-CNNs. Wireless Netw 30, 6125–6133 (2024). https://doi.org/10.1007/s11276-023-03392-8

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