期刊
ELECTRONICS LETTERS
卷 58, 期 11, 页码 426-428出版社
WILEY
DOI: 10.1049/ell2.12475
关键词
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资金
- RAI-Radiotelevisione Italiana
- SmartData@PoliTO center
This work introduces MMCE-Net, a deep-learning tool aimed at improving the performance of video coding standards based on motion-compensation. The proposed method enhances the accuracy of motion-compensated frames to improve the coding efficiency and rate-distortion performance.
This work introduces the multiframe motion-compensation enhancement network (MMCE-Net), a deep-learning tool aimed at improving the performance of current video coding standards based on motion-compensation, such as H.265/HEVC. The proposed method improves the inter-prediction coding efficiency by enhancing the accuracy of the motion-compensated frame and thereby improving the rate-distortion performance. MMCE-Net is a neural network that jointly exploits the predicted coding unit and two co-located coding units from previous reference frames to improve the estimation of the temporal evolution of the scene. This letter describes the architecture of MMCE-Net, how it is integrated into H.265/HEVC and the corresponding performance.
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