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Conference Paper: Content Adaptive and Error Propagation Aware Deep Video Compression
Title | Content Adaptive and Error Propagation Aware Deep Video Compression |
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Authors | |
Issue Date | 2020 |
Publisher | Springer |
Citation | 16th European Conference on Computer Vision (ECCV 2020), Glasgow, UK, 23-28 August 2020. In Vedaldi, A, Bischof, H, Brox, T, et al. (Eds.), Computer Vision - ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part II, p. 456-472. Cham: Springer, 2020. How to Cite? |
Abstract | Recently, learning based video compression methods attract increasing attention. However, the previous works suffer from error propagation due to the accumulation of reconstructed error in inter predictive coding. Meanwhile, the previous learning based video codecs are also not adaptive to different video contents. To address these two problems, we propose a content adaptive and error propagation aware video compression system. Specifically, our method employs a joint training strategy by considering the compression performance of multiple consecutive frames instead of a single frame. Based on the learned long-term temporal information, our approach effectively alleviates error propagation in reconstructed frames. More importantly, instead of using the hand-crafted coding modes in the traditional compression systems, we design an online encoder updating scheme in our system. The proposed approach updates the parameters for encoder according to the rate-distortion criterion but keeps the decoder unchanged in the inference stage. Therefore, the encoder is adaptive to different video contents and achieves better compression performance by reducing the domain gap between the training and testing datasets. Our method is simple yet effective and outperforms the state-of-the-art learning based video codecs on benchmark datasets without increasing the model size or decreasing the decoding speed. |
Persistent Identifier | http://hdl.handle.net/10722/321913 |
ISBN | |
ISSN | 2020 SCImago Journal Rankings: 0.249 |
Series/Report no. | Lecture Notes in Computer Science ; 12347 LNCS Sublibrary. SL 6, Image Processing, Computer Vision, Pattern Recognition, and Graphics |
DC Field | Value | Language |
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dc.contributor.author | Lu, Guo | - |
dc.contributor.author | Cai, Chunlei | - |
dc.contributor.author | Zhang, Xiaoyun | - |
dc.contributor.author | Chen, Li | - |
dc.contributor.author | Ouyang, Wanli | - |
dc.contributor.author | Xu, Dong | - |
dc.contributor.author | Gao, Zhiyong | - |
dc.date.accessioned | 2022-11-03T02:22:19Z | - |
dc.date.available | 2022-11-03T02:22:19Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | 16th European Conference on Computer Vision (ECCV 2020), Glasgow, UK, 23-28 August 2020. In Vedaldi, A, Bischof, H, Brox, T, et al. (Eds.), Computer Vision - ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part II, p. 456-472. Cham: Springer, 2020. | - |
dc.identifier.isbn | 9783030585358 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/321913 | - |
dc.description.abstract | Recently, learning based video compression methods attract increasing attention. However, the previous works suffer from error propagation due to the accumulation of reconstructed error in inter predictive coding. Meanwhile, the previous learning based video codecs are also not adaptive to different video contents. To address these two problems, we propose a content adaptive and error propagation aware video compression system. Specifically, our method employs a joint training strategy by considering the compression performance of multiple consecutive frames instead of a single frame. Based on the learned long-term temporal information, our approach effectively alleviates error propagation in reconstructed frames. More importantly, instead of using the hand-crafted coding modes in the traditional compression systems, we design an online encoder updating scheme in our system. The proposed approach updates the parameters for encoder according to the rate-distortion criterion but keeps the decoder unchanged in the inference stage. Therefore, the encoder is adaptive to different video contents and achieves better compression performance by reducing the domain gap between the training and testing datasets. Our method is simple yet effective and outperforms the state-of-the-art learning based video codecs on benchmark datasets without increasing the model size or decreasing the decoding speed. | - |
dc.language | eng | - |
dc.publisher | Springer | - |
dc.relation.ispartof | Computer Vision - ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part II | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science ; 12347 | - |
dc.relation.ispartofseries | LNCS Sublibrary. SL 6, Image Processing, Computer Vision, Pattern Recognition, and Graphics | - |
dc.title | Content Adaptive and Error Propagation Aware Deep Video Compression | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-030-58536-5_27 | - |
dc.identifier.scopus | eid_2-s2.0-85097251874 | - |
dc.identifier.spage | 456 | - |
dc.identifier.epage | 472 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.publisher.place | Cham | - |