# Versatile Video Coding-Post Processing Feature Fusion:
## 1 Introduction
The rise of deep learning has led to a rise in the development of revolutionary image and video quality enhancement techniques, prominently featuring Convolutional Neural Networks (CNNs). This has opened new avenues for significantly enhancing visual quality, thereby expanding the possibilities in image and video processing. Many methods for improving image and video quality using CNNs have been developed with the rise of deep learning. Several CNN-based image enhancement techniques have been proposed to eliminate artifacts and blocking effects in Joint Photographic Experts Group (JPEG) compressed images, as seen in references [3; 4; 5; 6; 7; 8]. Several approaches, including those in [9; 10; 11; 12; 13; 14], have been proposed to enhance the quality of HEVC-compressed videos. Recently, efforts have also been made to boost the quality of VVC-compressed visual content, targeting better image quality [15; 16; 17; 18; 19; 20; 21]. Despite the encouraging outcomes of previous studies, significant limitations and opportunities for enhancement remain, necessitating continued investigation and innovation to overcome these challenges and unlock further advancements. The sophistication of previous architectures has not translated to meaningful advancements, highlighting a disconnect between design complexity and practical benefits. The innovation of neural network-based post-process filters resides in their ability to apply deep learning principles to upgrade decoded video quality, without requiring changes to the video coding framework. Also, the in-loop filter approach is plagued by several issues. In-loop filters in video processing can have several drawbacks. They often rely on motion estimation, which can lead to over-smoothing and loss of important details, especially in complex scenes. This can result in decreased video quality and the introduction of artifacts. Additionally, in-loop filters can cause temporal inconsistencies, where the quality of the filtered video varies across frames. The use of pre-defined parameters and thresholds can also lead to suboptimal filtering for certain types of video content. Furthermore, the aggressive application of in-loop filters can increase the bitrate of the video, leading to larger file sizes and reduced encoding efficiency. Neural network-based post-process filters work independently of the video encoding and decoding process, making them easy to implement and flexible to use. Unlike traditional filters, they do not impact the video's bitrate or encoding efficiency, which means videos can be compressed to a smaller size without losing quality. These filters are particularly effective at enhancing the quality of complex or dynamic video content. Additionally, they can be easily customized to work with different video formats, resolutions, and frame rates, making them a more versatile solution than traditional filters. In this paper we introduce a Convolutional NeuralNetwork-based Post-Processing Filter (VVC-PPFF) that leverages the integration of dense features and quantization parameter (QP) maps to achieve improved results. The goal of VVC-PPFF is to learn and extract valuable residual details, thereby enhancing the quality of the input image that has undergone compression. The reconstructed image is strengthened with the QP map [22], which serves as an additional input channel to the CNN network, providing it with explicit information about the compression level and artifact distribution. A feature fusion mechanism is integrated into the proposed network to facilitate more effective feature sharing and propagation, thereby enhancing the overall representation power of the network. We utilized a skip connection between the input reconstructed image and the network residuals. The main contributions of this paper can be summarized as follows:  
1.