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[bibtex]@InProceedings{Pham_2021_CVPR, author = {Pham, Chi D. K. and Fu, Chen and Zhou, Jinjia}, title = {Deep Learning Based Spatial-Temporal In-Loop Filtering for Versatile Video Coding}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2021}, pages = {1861-1865} }
Deep Learning Based Spatial-Temporal In-Loop Filtering for Versatile Video Coding
Abstract
The existing deep learning-based Versatile Video Coding (VVC) in-loop filtering (ILF) enhancement works mainly focus on learning the one-to-one mapping between the reconstructed and the original video frame, ignoring the potential resources at encoder and decoder. This work proposes a deep learning-based Spatial-Temporal In-Loop filtering (STILF) that takes advantage of the coding information to improve VVC in-loop filtering. Each CTU is filtered by VVC default in-loop filtering, self-enhancement Convolutional neural network (CNN) with CU map (SEC), and the reference-based enhancement CNN with the optical flow (REO). Bits indicating ILF mode are encoded under CABAC regular mode. Experimental results show that 3.78%, 6.34%, 6%, and 4.64% BD-rate reductions are obtained under All Intra, Low Delay P, Low Delay B, and Random Access configurations, respectively.
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