论文标题
视频编码中基于CNN的过滤器的QP自适应机制
A QP-adaptive Mechanism for CNN-based Filter in Video Coding
论文作者
论文摘要
基于卷积的神经网络（CNN）过滤器在视频编码方面取得了巨大成功。但是，在大多数以前的工作中，每个量化参数（QP）频段都需要单个模型。本文提出了一种通用方法，可以帮助任意CNN滤波器处理不同的量化噪声。我们对量化噪声问题进行建模，并在CNN上实现可行的解决方案，该解决方案将量化步骤（QSTEP）引入卷积。当量化噪声增加时，CNN滤波器抑制噪声的能力会相应提高。该方法可直接用于替换任何现有CNN滤波器中的（香草）卷积层。通过仅使用25％的参数，所提出的方法比使用具有VTM-6.3锚的多个模型更好的性能。此外，通过我们提出的色度成分方法，还可以实现0.2％的额外的BD率降低。
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.
