Recent trending on learning based video compression: A survey
The increase of video content and video resolution drive more exploration of video compression techniques recently. Meanwhile, learning-based video compression is receiving much attention over the past few years because of its content adaptivity and parallelable computation. Although several promisi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co. Ltd.
2021-01-01
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| Series: | Cognitive Robotics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667241321000148 |
| _version_ | 1828086039631101952 |
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| author | Trinh Man Hoang, M.E Jinjia Zhou, PhD |
| author_facet | Trinh Man Hoang, M.E Jinjia Zhou, PhD |
| author_sort | Trinh Man Hoang, M.E |
| collection | DOAJ |
| description | The increase of video content and video resolution drive more exploration of video compression techniques recently. Meanwhile, learning-based video compression is receiving much attention over the past few years because of its content adaptivity and parallelable computation. Although several promising reports were introduced, there is no breakthrough work that can further go out of the research area. In this work, we provide an up-to-date overview of learning-based video compression research and its milestones. In particular, the research idea of recent works on learning-based modules for conventional codec adaption and the learning-based end-to-end video compression are reported along with their advantages and disadvantages. According to the review, compare to the current video compression standard like HEVC or VVC, from 3% to 12% BD-rate reduction have been achieved with integrated approaches while outperformed results on perceptual quality and structure similarity were reported for end-to-end approaches. Furthermore, the future research suggestion is provided based on the current obstacles. We conclude that, for a long-term benefit, the computation complexity is the major problem that needed to be solved, especially on the decoder-end. Whereas the rate-dependent and generative designs are optimistic to provide a more low-complex efficient learning-based codec. |
| first_indexed | 2024-04-11T04:49:30Z |
| format | Article |
| id | doaj.art-0901ce047c9c4316b92af87d8d237ead |
| institution | Directory Open Access Journal |
| issn | 2667-2413 |
| language | English |
| last_indexed | 2024-04-11T04:49:30Z |
| publishDate | 2021-01-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Cognitive Robotics |
| spelling | doaj.art-0901ce047c9c4316b92af87d8d237ead2022-12-27T04:41:32ZengKeAi Communications Co. Ltd.Cognitive Robotics2667-24132021-01-011145158Recent trending on learning based video compression: A surveyTrinh Man Hoang, M.E0Jinjia Zhou, PhD1Graduate School of Science and Engineering, Hosei University, Tokyo 1848584, JapanGraduate School of Science and Engineering, Hosei University, Tokyo 1848584, Japan; JST, PRESTO, Tokyo, Japan; Corresponding author.The increase of video content and video resolution drive more exploration of video compression techniques recently. Meanwhile, learning-based video compression is receiving much attention over the past few years because of its content adaptivity and parallelable computation. Although several promising reports were introduced, there is no breakthrough work that can further go out of the research area. In this work, we provide an up-to-date overview of learning-based video compression research and its milestones. In particular, the research idea of recent works on learning-based modules for conventional codec adaption and the learning-based end-to-end video compression are reported along with their advantages and disadvantages. According to the review, compare to the current video compression standard like HEVC or VVC, from 3% to 12% BD-rate reduction have been achieved with integrated approaches while outperformed results on perceptual quality and structure similarity were reported for end-to-end approaches. Furthermore, the future research suggestion is provided based on the current obstacles. We conclude that, for a long-term benefit, the computation complexity is the major problem that needed to be solved, especially on the decoder-end. Whereas the rate-dependent and generative designs are optimistic to provide a more low-complex efficient learning-based codec.http://www.sciencedirect.com/science/article/pii/S2667241321000148Video compressionDeep learningLearning-based compression |
| spellingShingle | Trinh Man Hoang, M.E Jinjia Zhou, PhD Recent trending on learning based video compression: A survey Cognitive Robotics Video compression Deep learning Learning-based compression |
| title | Recent trending on learning based video compression: A survey |
| title_full | Recent trending on learning based video compression: A survey |
| title_fullStr | Recent trending on learning based video compression: A survey |
| title_full_unstemmed | Recent trending on learning based video compression: A survey |
| title_short | Recent trending on learning based video compression: A survey |
| title_sort | recent trending on learning based video compression a survey |
| topic | Video compression Deep learning Learning-based compression |
| url | http://www.sciencedirect.com/science/article/pii/S2667241321000148 |
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