To enhance the video quality after encoding and decoding in video compression,a video quality enhancement framework is pro-posed based on local and non-local priors in this paper.Low-level features are first extracted...To enhance the video quality after encoding and decoding in video compression,a video quality enhancement framework is pro-posed based on local and non-local priors in this paper.Low-level features are first extracted through a single convolution layer and then pro-cessed by several conv-tran blocks(CTB)to extract high-level features,which are ultimately transformed into a residual image.The final re-constructed video frame is obtained by performing an element-wise addition of the residual image and the original lossy video frame.Experi-ments show that the proposed Conv-Tran Network(CTN)model effectively recovers the quality loss caused by Versatile Video Coding(VVC)and further improves VVC's performance.展开更多
基金supported by the Key R&D Program of China under Grant No. 2022YFC3301800Sichuan Local Technological Development Program under Grant No. 24YRGZN0010ZTE Industry-University-Institute Cooperation Funds under Grant No. HC-CN-03-2019-12
文摘To enhance the video quality after encoding and decoding in video compression,a video quality enhancement framework is pro-posed based on local and non-local priors in this paper.Low-level features are first extracted through a single convolution layer and then pro-cessed by several conv-tran blocks(CTB)to extract high-level features,which are ultimately transformed into a residual image.The final re-constructed video frame is obtained by performing an element-wise addition of the residual image and the original lossy video frame.Experi-ments show that the proposed Conv-Tran Network(CTN)model effectively recovers the quality loss caused by Versatile Video Coding(VVC)and further improves VVC's performance.
