Learned Video Compression With Efficient Temporal Context Learning
IEEE Trans. Image Process.(2023)
摘要
In contrast to image compression, the key of video compression is to efficiently exploit the temporal context for reducing the inter-frame redundancy. Existing learned video compression methods generally rely on utilizing short-term temporal correlations or image-oriented codecs, which prevents further improvement of the coding performance. This paper proposed a novel temporal context-based video compression network (TCVC-Net) for improving the performance of learned video compression. Specifically, a global temporal reference aggregation (GTRA) module is proposed to obtain an accurate temporal reference for motion-compensated prediction by aggregating long-term temporal context. Furthermore, in order to efficiently compress the motion vector and residue, a temporal conditional codec (TCC) is proposed to preserve structural and detailed information by exploiting the multi-frequency components in temporal context. Experimental results show that the proposed TCVC-Net outperforms public state-of-the-art methods in terms of both PSNR and MS-SSIM metrics.
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关键词
Image coding,Video compression,Transforms,Codecs,Quantization (signal),Video coding,Motion compensation,Learned video compression,inter-frame prediction,long-term correspondence,temporal context learning,TCVC-Net
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