# Versatile Video Coding-Post Processing Feature Fusion:
## 2 Related Works
### Deep Learning-Based Artifact Removal Methods  
Deep learning has made significant strides in various fields, and its application in image and video quality enhancement has gained popularity among researchers, yielding promising results. In [17], [PERSON] et al. introduced a Dense Residual Convolutional Neural Network (DRN) as an in-loop filter for VVC, which leverages residual learning, dense shortcuts, and bottleneck layers to address gradient vanishing, promote feature reuse, and reduce computational costs. [PERSON] et al. [15] introduced a Variable CNN (VCNN) in-loop filter for VVC, capable of handling compressed videos with varying quality parameters (QPs) and frame types (FTs) using a single model. The filter features an attention module that adaptively adjusts features based on QPs or FTs, which is integrated into the residual block to leverage informative features. Additionally, a residual feature aggregation module (RFA) is employed to minimize information loss and enhance feature extraction efficiency. In [16], [PERSON] et al. proposed a CNN filter designed to improve the quality of VVC intra-coded frames, utilizing additional information such as partitioning and prediction data as inputs. For chroma, the auxiliary information also incorporates luma samples. The objective is to enhance the visual quality of VVC intra-coded frames by leveraging this supplementary information in the CNN-based filter. In [30], [PERSON] et al. proposed a CNN-based in-loop filter for VVC to reduce compression artifacts. This filter uses CNN features from DCT-transformed input to extract high-frequency components and introduces long-range correlation into spatial CNN features through multi-stage feature fusion. [PERSON] et al. [20] introduced a new CNN-based post-processing method for VVC in the RA configuration. To optimize performance, the network was trained on a large dataset of VVC-compressed videos at various resolutions and quality settings. The trained network is applied at the decoder to enhance the reconstruction quality of VVC-encoded videos. In [21], [PERSON] et al. introduced MFRNet, a CNN-based network for post-processing and in-loop filtering in video compression. MFRNet consists of four multi-level feature review residual dense blocks (MFRBs), which extract features from multiple convolutional layers using a residual learning structure with dense connections. This design enables the reuse of high-dimensional features from previous blocks, improving feature reuse and capturing information flow between blocks. In [18], [PERSON] et al. explored a learning-based approach as a post-processing step to improve the quality of decoded VVC videos. They employed multitask learning to develop a single network that can simultaneously perform quality enhancement and super-resolution, optimized to handle various levels of degradation. In [31], [PERSON] et al. proposed a content-adaptive CNN-based post-processing filter to enhance the quality of VVC decoded videos. The filter was initially trained on a general dataset of video sequences and then fine-tuned on the specific test video sequence to adapt to its unique content. [PERSON] et al. [32] introduced a deep learning-based approach called Spatial-Temporal In-Loop Filtering (STILF) that leverages coding information to enhance VVC in-loop filtering. The proposed method learns to map reconstructed video frames to their original counterparts, utilizing a combination of VVC default filtering, a self-enhancement CNN with a CU map (SEC), and a reference-based enhancement CNN with optical flow (REO) for each CTU. In [33] [PERSON] et al. proposed a generalized in-loop filter that builds upon the ALF in VVC by integrating a CNN. In this method, kernels are dynamically selected from a set of trained kernels based on fixed classifications across multiple layers, thereby reducing the computational complexity of the CNN-based in-loop filter. [PERSON] et al. [34] proposed a CNN-based in-loop filter for VVC intra coding, featuring a lightweight and efficient design.