# Versatile Video Coding-Post Processing Feature Fusion:
## 2 Related Works
### Conventional Artifact Removal Methods  
Conventional techniques were initially designed to alleviate the artifacts introduced by image compression, with the goal of restoring the original image quality. In [23], [PERSON] et al. introduced a novel post-processing approach that takes into account the masking effect of the human visual system (HVS), incorporating an adaptive weighting mechanism to optimize the post-filtering process and improve image quality. This approach utilizes a large window to effectively reduce artifacts in low-activity regions, where blocking artifacts are more visually prominent. To preserve image details, a small mask and a large central weight are applied to high-activity blocks, where the human visual system's masking ability helps conceal blocking artifacts amidst complex local backgrounds. [PERSON] et al. [24]proposed an innovative deblocking algorithm that leverages an adaptive non-local means filter to effectively eliminate blocking artifacts in images compressed using Block Discrete Cosine Transform (BDCT). Blocks are used as the fundamental processing unit, and their values are estimated by taking a weighted sum of neighboring blocks. The weights are dynamically adjusted based on the content of each block and the level of quantization noise present, allowing for adaptive processing. [PERSON] et al. [25] introduced an approach to image filtering, utilizing the Shape-Adaptive Discrete Cosine Transform (SA-DCT) to effectively remove blocking and ringing artifacts from images compressed using block-DCT methods. This approach involves using the threshold or attenuated SA-DCT coefficients to generate a localized approximation of the original signal, confined within a dynamically determined support region that adapts to the signal's shape. In [26], [PERSON] et al. developed a non-iterative deblocking algorithm that leverages wavelet technology to simultaneously mitigate both blocking and ringing artifacts in compressed images. The proposed algorithm is grounded in a theoretical understanding of blocking artifacts, allowing it to effectively address the statistical properties of block discontinuities and the scale-based behavior of wavelet coefficients for various image features, thereby reducing both blocking and ringing artifacts. [PERSON] et al. [27] presents an innovative method for restoring JPEG compressed images, which leverages the residual redundancies present in JPEG code streams and the sparse nature of underlying images, to effectively recover the original image. The proposed approach tackles image restoration by directly recovering the DCT coefficients of the original image, thereby preventing the propagation of quantization errors into the pixel domain. Additionally, it utilizes online machine-learned local spatial features to constrain the solution of the inverse problem, ensuring a more accurate restoration. In [28], [PERSON] et al. introduces a new approach to alleviate blocking artifacts in block-coded images by combining patch clustering and low-rank minimization. This method leverages both local and non-local sparse representations in a single framework, effectively reducing the visibility of block boundaries and improving overall image quality. The compressed image is split into small segments, and then similar segments are grouped together through clustering. Next, these grouped segments are jointly refined using a low-rank minimization technique known as the Singular Value Thresholding (SVT) algorithm, which helps to enhance image quality. [PERSON] et al. [29] presents an innovative technique to alleviate compression artifacts by leveraging non-local similarities to estimate transform coefficients for overlapping blocks. The proposed method estimates DCT coefficients for each block by combining two predictions: the quantized coefficients from the compressed bitstream and a weighted average of coefficients from similar non-local blocks.