  This paper presents a framework for Convolutional Neural Network (CNN)-based
quality enhancement task, by taking advantage of coding information in the
compressed video signal. The motivation is that normative decisions made by the
encoder can significantly impact the type and strength of artifacts in the
decoded images. In this paper, the main focus has been put on decisions
defining the prediction signal in intra and inter frames. This information has
been used in the training phase as well as input to help the process of
learning artifacts that are specific to each coding type. Furthermore, to
retain a low memory requirement for the proposed method, one model is used for
all Quantization Parameters (QPs) with a QP-map, which is also shared between
luma and chroma components. In addition to the Post Processing (PP) approach,
the In-Loop Filtering (ILF) codec integration has also been considered, where
the characteristics of the Group of Pictures (GoP) are taken into account to
boost the performance. The proposed CNN-based Quality Enhancement(QE) framework
has been implemented on top of the VVC Test Model (VTM-10). Experiments show
that the prediction-aware aspect of the proposed method improves the coding
efficiency gain of the default CNN-based QE method by 1.52%, in terms of BD-BR,
at the same network complexity compared to the default CNN-based QE filter.
