## 1 Introduction  
H.266/VVC [1], a new video coding standard, offers higher compression rates than H.265/HEVC while maintaining quality. It supports 8K Ultra HD (shown in Figure 1), HDR content, and 360-degree video [2] and is optimized for streaming services and real-time communications that require dynamic bandwidth and resolution adjustments. VVC improves compression efficiency, compatibility, and flexibility for emerging video formats and application scenarios. Video coding [3] improves storage and transmission efficiency by reducing redundant information. It uses prediction techniques to analyze similarities between frames (interframe) or within frames (intra-frame), leaving'residuals'. These residuals are orthogonally transformed into the frequency domain, producing coefficients that represent different frequency components. Quantization simplifies these coefficients for storage and transmission, with the quantization parameter (QP) [4] determining the degree of simplification  
Figure 1: 8K video transmission system. (shown in Figure 2). A higher QP results in less accurate quantization, which reduces the amount of data and bandwidth required but compromises quality. Conversely, a lower QP increases data volume but improves video quality. Thus, video coding reduces data volume through prediction and quantization and balances video clarity and data requirements by adjusting the QP. Video encoding and decoding are critical to video transmission, with the quantization parameter (QP) controlling data transmission and affecting picture quality. The video signal is de-redundant before coding and then converted to the frequency domain, where the coefficients are quantized and transmitted, resulting in a loss of quality. A number of potential solutions have been continuously proposed to improve this problem. The existing methods can be grouped into three dominant categories: interpolation-based, reconstruction-based, and learning-based. The fundamental principle of interpolation is to estimate the value of an unknown pixel point by employing a numerical relationship between known pixel points. This process is based on the concept of signal processing, whereby the data from neighboring pixels is utilized to make inferences and thereby obtain a restored image. The most basic form of interpolation is linear interpolation. In this method, the pixel value is assumed to be a linear function of the position between two neighboring pixels, A and B. This allows the interpolated pixel value to be calculated using the pixel value and the position between A and B. Accordingly, the pixel value at any position between A and B can be calculated based on this linear relationship. Higher-order interpolation methods, such as bilinear and bicubic interpolation, consider additional information about neighboring pixels. Bilinear interpolation considers the linear relationship in both directions, building upon the principles of linear interpolation. In the case of a pixel situated within a rectangular region, the pixel values at any given position within the region can be calculated by linear interpolation in two directions based on the pixel values of the four vertices. Bicubic interpolation introduces a greater number of pixel points than bilinear interpolation, which takes into account information from a larger range of pixel values, thus improving the accuracy of the interpolation. The method typically employs more complex weighting functions. A reconstruction-based algorithm is a type of image reconstruction that introduces a priori knowledge during the reconstruction process to achieve a locally optimal solution. This involves the use of frequency domain, time domain, and other multi-scale mapping  
Figure 2: Control of video decoding by quantization parameters. relationships to predict the results of the newly generated image.