  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.
