H.266/VVC(Versatile Video Coding)是目前最为先进的视频编码标准,其为了适应更加复杂纹理的视频图像,在上一代视频编码标准H.265/HEVC(High Efficiency Video Coding)基础上引入了带有嵌套多类型树的四叉树,使得图像的块划分灵活,在...H.266/VVC(Versatile Video Coding)是目前最为先进的视频编码标准,其为了适应更加复杂纹理的视频图像,在上一代视频编码标准H.265/HEVC(High Efficiency Video Coding)基础上引入了带有嵌套多类型树的四叉树,使得图像的块划分灵活,在提升编码性能的同时带来了显著增加的计算量。针对以上问题,提出了基于Sobel边缘检测的帧内快速CU划分技术,利用边缘特征来跳过几种CU划分方式达到减少运算量的效果,在AI(All In⁃tra)模式下,编码时间平均减少了39.95%,而BDBR(Bjøntegaard Delta Bitrate)仅上升了0.73%。展开更多
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
文摘H.266/VVC(Versatile Video Coding)是目前最为先进的视频编码标准,其为了适应更加复杂纹理的视频图像,在上一代视频编码标准H.265/HEVC(High Efficiency Video Coding)基础上引入了带有嵌套多类型树的四叉树,使得图像的块划分灵活,在提升编码性能的同时带来了显著增加的计算量。针对以上问题,提出了基于Sobel边缘检测的帧内快速CU划分技术,利用边缘特征来跳过几种CU划分方式达到减少运算量的效果,在AI(All In⁃tra)模式下,编码时间平均减少了39.95%,而BDBR(Bjøntegaard Delta Bitrate)仅上升了0.73%。
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.
