Deep motion‐compensation enhancement in video compression
| Autor: | N. Prette, D. Valsesia, T. Bianchi, E. Magli, M. Naccari, A. Fiandrotti |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Electronics Letters, Vol 58, Iss 11, Pp 426-428 (2022) |
| Druh dokumentu: | article |
| ISSN: | 1350-911X 0013-5194 |
| DOI: | 10.1049/ell2.12475 |
| Popis: | Abstract 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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