학술논문
A QP-adaptive Mechanism for CNN-based Filter in Video Coding
Document Type
Conference
Author
Source
2022 IEEE International Symposium on Circuits and Systems (ISCAS) Circuits and Systems (ISCAS), 2022 IEEE International Symposium on. :3195-3199 May, 2022
Subject
Language
ISSN
2158-1525
Abstract
Convolutional neural network (CNN)-based in-loop filtering have been very successful in video coding. For most existing works, however, a specific model was required for each quantization parameter (QP) band. In this paper, we introduce a generic method for helping CNN-filters deal with variable quantization noises. A feasible solution to this problem can be implemented on CNN by introducing a quantization step (Qstep) into the CNN. As the quantization noise changes, the CNN filter’s ability to suppress noise changes accordingly. The (vanilla) convolution layer can be replaced directly by this method in existing CNN filters. Compared with the VVenC anchor, only one CNN filter is used and achieves about 3.6% BD-rate reduction for the luminance component of random-access configuration. Also, about 0.8% BD-rate reduction has been achieved compared with the previous QP-map method.