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- W4313443383 abstract "This paper introduces a learned hierarchical B-frame coding scheme in response to the Grand Challenge on Neural Network-based Video Coding at ISCAS 2023. We address specifically three issues, including (1) B-frame coding, (2) YUV 4:2:0 coding, and (3) content-adaptive variable-rate coding with only one single model. Most learned video codecs operate internally in the RGB domain for P-frame coding. B-frame coding for YUV 4:2:0 content is largely under-explored. In addition, while there have been prior works on variable-rate coding with conditional convolution, most of them fail to consider the content information. We build our scheme on conditional augmented normalized flows (CANF). It features conditional motion and inter-frame codecs for efficient B-frame coding. To cope with YUV 4:2:0 content, two conditional inter-frame codecs are used to process the Y and UV components separately, with the coding of the UV components conditioned additionally on the Y component. Moreover, we introduce adaptive feature modulation in every convolutional layer, taking into account both the content information and the coding levels of B-frames to achieve content-adaptive variable-rate coding. Experimental results show that our model outperforms x265 and the winner of last year's challenge on commonly used datasets in terms of PSNR-YUV." @default.
- W4313443383 created "2023-01-06" @default.
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- W4313443383 date "2022-12-29" @default.
- W4313443383 modified "2023-09-27" @default.
- W4313443383 title "Learned Hierarchical B-frame Coding with Adaptive Feature Modulation for YUV 4:2:0 Content" @default.
- W4313443383 doi "https://doi.org/10.48550/arxiv.2212.14187" @default.
- W4313443383 hasPublicationYear "2022" @default.
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