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Multiscale convolutional neural networks for in-loop video restoration

Citation Author(s):
Kiran Misra, Andrew Segall, Byeongdoo Choi
Submitted by:
Kiran Misra
Last updated:
2 March 2023 - 3:21am
Document Type:
Presentation Slides
Document Year:
Kiran M. Misra
Paper Code:


Incorporating neural networks into a video codec as an in-loop filter has been shown to provide significant improvements in coding efficiency. Unfortunately, the computational complexity associated with the neural network, specifically the number of multiply-accumulate (MAC) operations, makes these approaches intractable in practice. In this paper, we consider using a multiscale approach to reduce complexity while maintaining coding efficiency. Experimental results demonstrate a 5.4× reduction in MAC operations while achieving an average bit rate savings of 6.4% and 6.3% for all intra and random access coding, respectively, when compared to the evolving AV2 standard. Ablation studies are also provided and show that the approach achieves all but 0.2% of the coding efficiency of full resolution processing.

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Multiscale convolutional neural networks for in-loop video restoration - slides.pdf