A QP-adaptive Mechanism for CNN-based Filter in Video Coding

• URL: http://arxiv.org/abs/2010.13059v1
• Date: Sun, 25 Oct 2020 08:02:38 GMT
• Title: A QP-adaptive Mechanism for CNN-based Filter in Video Coding
• Authors: Chao Liu and Heming Sun and Jiro Katto and Xiaoyang Zeng and Yibo Fan
• Abstract summary: This paper presents a generic method to help an arbitrary CNN-filter handle different quantization noise. When the quantization noise increases, the ability of the CNN-filter to suppress noise improves accordingly. An additional BD-rate reduction of 0.2% is achieved by our proposed method for chroma components.
• Score: 26.1307267761763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolutional neural network (CNN)-based filters have achieved great success in video coding. However, in most previous works, individual models are needed for each quantization parameter (QP) band. This paper presents a generic method to help an arbitrary CNN-filter handle different quantization noise. We model the quantization noise problem and implement a feasible solution on CNN, which introduces the quantization step (Qstep) into the convolution. When the quantization noise increases, the ability of the CNN-filter to suppress noise improves accordingly. This method can be used directly to replace the (vanilla) convolution layer in any existing CNN-filters. By using only 25% of the parameters, the proposed method achieves better performance than using multiple models with VTM-6.3 anchor. Besides, an additional BD-rate reduction of 0.2% is achieved by our proposed method for chroma components.

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