# Versatile Video Coding-Post Processing Feature Fusion:
## 1 Introduction
The block-based approach in VVC can result in visible block boundaries (e.g., blockiness), which appears as a grid-like pattern on the video. This occurs when blocks are misaligned or when the compression algorithm creates discontinuities between blocks, making it more noticeable in areas with smooth textures or uniform colors. In VVC, the Discrete Cosine Transform (DCT) is employed to transform blocks of pixels from the spatial domain into the frequency domain, where they are represented as coefficients, enabling efficient compression and processing of the video data. The DCT transformation in VVC can sometimes produce unwanted visual effects, including oscillations or ringing artifacts around edges, which appear as concentric circles, and a buzzing or fizzing effect, causing distractions and annoyances in the video. Quantized DCT coefficients in VVC can result in two main issues: firstly, the loss of fine details and textures due to the reduction in coefficient values, causing a decrease in image clarity; and secondly, a reduction in bandwidth, which leads to the loss of high-frequency components, resulting in a softer, more blurred image. Intra-prediction method in VVC, which uses neighboring blocks to predict block values, can have two drawbacks: it can introduce directional artifacts, such as streaks or patterns, due to inaccurate predictions, and it can also cause over-smoothing, leading to a loss of details and textures in the decoded video. Inter-prediction, which predicts block values based on previous frames, can have two negative consequences: it can cause motion artifacts, such as blur, jerkiness, or ghosting, due to inaccurate motion estimation, and it can also lead to temporal artifacts, like flickering or flashing, which can be distracting and annoying. In VVC, traditional in-loop filters are employed to enhance visual quality by reducing artifacts and improving overall video clarity. DBF reduces blocking artifacts, which appear as visible block boundaries in the video. These artifacts occur when the video is divided into blocks for compression, and the boundaries between blocks become visible. SAO reduces ringing artifacts, which appear as oscillations or ringing around edges in the video. SAO also helps to reduce mosquito noise, which is a type of noise that appears as a buzzing or fizzing effect around edges. ALF reduces blurring artifacts, which occur when the video becomes overly smoothed or loses its sharpness. Despite being designed to enhance visual quality, traditional in-loop filters in VVC, such as ALF, DBF, and SAO, have inherent limitations that prevent them from fully eliminating decoded frame artifacts, resulting in residual distortions and compromised image quality. The limitations of traditional in-loop filters in VVC can be attributed to their imperfect artifact detection, which leads to incomplete distortion removal. Overly aggressive filtering can have the opposite effect, causing image blur and loss of details, thereby introducing new artifacts. Furthermore, these filters often struggle to adapt to diverse content, such as intricate textures or motion, resulting in inadequate artifact suppression. Moreover, the focus of the traditional in-loop filters is blocking and ringing artifacts, meaning that other types of distortions, including motion and temporal artifacts that can persist in decoded frames, can be ignored. Despite their widespread use, traditional filters in video coding still have untapped potential for enhancement. There are opportunities to further refine and optimize these filters to achieve better reconstruction quality, even with existing algorithms and architectures. The past few years have witnessed a remarkable surge in the adoption of deep learning-based filter approaches, driven primarily by their exceptional performance and efficacy. These innovative methods have garnered significant attention and acclaim, establishing themselves as a go-to solution in various domains.