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CONTENT-WEIGHTED DEEP RESIDUAL LEARNING FOR VIDEO IN-LOOP FILTERING
CONTENT-WEIGHTED DEEP RESIDUAL LEARNING FOR VIDEO IN-LOOP FILTERING
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机译:内容加权的深度残差学习,用于视频环路过滤
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摘要
Systems and methods are provided for improving filtering performance and Bjøntegaard-Delta (BD) rate savings for video processing. In addition to computing the artifacts between a given compressed image and a restored clean image after filtering using Deep Residual Learning (DRL) for recovering the residual between input and output, filtering strength of a loop filter may be controlled by the content of the region of the image, such that, in more important areas, such as the face and edges, the filtering strength may be increased while in less important areas, such as textures and backgrounds, the filtering strength may be decreased.
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