A quality enhancement network with coding priors for constant bit rate video coding
Knowledge-Based Systems, ISSN: 0950-7051, Vol: 258, Page: 110010
2022
- 2Citations
- 5Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Video coding can effectively compress data, while the introduction of compression artifacts degrades the visual quality and the performance of artificial intelligence video applications. Video quality enhancement (VQE) methods can improve compressed video quality without modifying the coding standard’s main modules. The existing VQE works mainly aim to improve video quality in the constant quantization parameter (CQP) coding mode. However, constant bit rate (CBR) coding mode is widely used in some streaming playback video applications, and VQE at CBR is more challenging than that at CQP. This article presents a novel Constant bit rate Video quality enhancement Network combined with Coding priors (CVCN). The proposed CVCN can be inserted into High Efficiency Video Coding (HEVC) codec as a CNN-based in-loop filter (LF) module or a post-processing module out of the codec. Moreover, we design a two-pass training strategy for the LF module to overcome multiple filtering. To adapt the QP diversity of CBR videos, we utilize the coding unit (CU)-wise QP prior by constructing a CU-wise QP adaptive module (CQAM) and a QP adaptive multi-scale residual block (QAMSRB) based on CQAM. We construct the CU-partition prior (CPP) by exploiting the relationship between the compression noise and CU partition information. A novel CPP spatial-attention block is proposed to combine the CPP with the self-attention module. Furthermore, the proposed CVCN can effectively enhance CBR videos at different bits per pixel (BPP) via a single model. Extensive experimental results show the superiority of the CVCN over state-of-the-art VQE approaches for CBR videos.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0950705122011030; http://dx.doi.org/10.1016/j.knosys.2022.110010; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85140921949&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0950705122011030; https://dx.doi.org/10.1016/j.knosys.2022.110010
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know