Benchmarking Conventional and Learned Video Codecs with a Low-Delay Configuration
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909495146315776 |
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| author | Teng, Siyue Jiang, Yuxuan Gao, Ge Zhang, Fan Davis, Thomas Liu, Zoe Bull, David |
| author_facet | Teng, Siyue Jiang, Yuxuan Gao, Ge Zhang, Fan Davis, Thomas Liu, Zoe Bull, David |
| contents | Recent advances in video compression have seen significant coding performance improvements with the development of new standards and learning-based video codecs. However, most of these works focus on application scenarios that allow a certain amount of system delay (e.g., Random Access mode in MPEG codecs), which is not always acceptable for live delivery. This paper conducts a comparative study of state-of-the-art conventional and learned video coding methods based on a low delay configuration. Specifically, this study includes two MPEG standard codecs (H.266/VVC VTM and JVET ECM), two AOM codecs (AV1 libaom and AVM), and two recent neural video coding models (DCVC-DC and DCVC-FM). To allow a fair and meaningful comparison, the evaluation was performed on test sequences defined in the AOM and MPEG common test conditions in the YCbCr 4:2:0 color space. The evaluation results show that the JVET ECM codecs offer the best overall coding performance among all codecs tested, with a 16.1% (based on PSNR) average BD-rate saving over AOM AVM, and 11.0% over DCVC-FM. We also observed inconsistent performance with the learned video codecs, DCVC-DC and DCVC-FM, for test content with large background motions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05042 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Benchmarking Conventional and Learned Video Codecs with a Low-Delay Configuration Teng, Siyue Jiang, Yuxuan Gao, Ge Zhang, Fan Davis, Thomas Liu, Zoe Bull, David Multimedia Computer Vision and Pattern Recognition Image and Video Processing Recent advances in video compression have seen significant coding performance improvements with the development of new standards and learning-based video codecs. However, most of these works focus on application scenarios that allow a certain amount of system delay (e.g., Random Access mode in MPEG codecs), which is not always acceptable for live delivery. This paper conducts a comparative study of state-of-the-art conventional and learned video coding methods based on a low delay configuration. Specifically, this study includes two MPEG standard codecs (H.266/VVC VTM and JVET ECM), two AOM codecs (AV1 libaom and AVM), and two recent neural video coding models (DCVC-DC and DCVC-FM). To allow a fair and meaningful comparison, the evaluation was performed on test sequences defined in the AOM and MPEG common test conditions in the YCbCr 4:2:0 color space. The evaluation results show that the JVET ECM codecs offer the best overall coding performance among all codecs tested, with a 16.1% (based on PSNR) average BD-rate saving over AOM AVM, and 11.0% over DCVC-FM. We also observed inconsistent performance with the learned video codecs, DCVC-DC and DCVC-FM, for test content with large background motions. |
| title | Benchmarking Conventional and Learned Video Codecs with a Low-Delay Configuration |
| topic | Multimedia Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2408.05042 |