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Loop filters are used in video coding to remove artifacts or improveperformance. Recent advances in deploying convolutional neural network (CNN) toreplace traditional loop filters show large gains but with problems forpractical application. First, different model is used for frames encoded withdifferent quantization parameter (QP), respectively. It is expensive forhardware. Second, float points operation in CNN leads to inconsistency betweenencoding and decoding across different platforms. Third, redundancy within CNNmodel consumes precious computational resources.  This paper proposes a CNN as the loop filter for intra frames and proposes ascheme to solve the above problems. It aims to design a single CNN model withlow redundancy to adapt to decoded frames with different qualities and ensureconsistency. To adapt to reconstructions with different qualities, bothreconstruction and QP are taken as inputs. After training, the obtained modelis compressed to reduce redundancy. To ensure consistency, dynamic fixed points(DFP) are adopted in testing CNN. Parameters in the compressed model are firstquantized to DFP and then used for inference of CNN. Outputs of each layer inCNN are computed by DFP operations. Experimental results on JEM 7.0 report3.14%, 5.21%, 6.28% BD-rate savings for luma and two chroma components with allintra configuration when replacing all traditional filters.

A practical convolutional neural network as loop filter for intra frame