SWGNet: Step-Wise Reference Frame Generation Network for Multiview Video Coding
IEEE Transactions on Circuits and Systems for Video Technology(2023)
摘要
In multiview video coding, the coding performance highly depends on the quality of the reference frames. In view of this, a step-wise reference frame generation network (SWGNet) is designed to improve the quality of the reference frame for efficient multiview video coding. In particular, a frame-level to block-level learning paradigm is proposed to step-wisely generate a high-quality reference frame. In the frame-level stage, by exploiting parallax correlations between temporal and inter-view references on the basis of image alignment, a parallax-guided frame-level synthesis module is proposed to generate an elementary reference frame. Then, in the block-level stage, a transformer-based block-level aggregation module is designed to further refine the texture details of the reference frame by modeling long-range dependencies among pixels. The proposed SWGNet is integrated into 3D-HEVC, and extensive experiments demonstrate that the proposed method achieves significant bitrate saving compared with 3D-HEVC.
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关键词
Reference frame generation,Inter-view correlation,Multiview video coding,Convolutional neural network
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