# Versatile Video Coding-Post Processing Feature Fusion:
## 3 Proposed Method
### Influence of QP Map  
The quantization parameter (QP) is a critical setting in video compression that balances video quality and file size. It is a single value that determines how much data is kept or discarded during compression. A lower QP setting preserves more video details, resulting in a higher quality video, but at the cost of a larger file size. On the other hand, a higher QP setting reduces the file size, but at the expense of video quality, as more data are discarded during compression.  
A QP map is a technique that dynamically adjusts the quantization parameter based on the unique features of each region within the video. This adaptive approach enables more effective compression, as it allocates more bits to areas of high importance, such as faces or textures, and fewer bits to less critical regions, for example backgrounds or skies. This results in the better preservation of details in areas that matter most, while maintaining an optimal balance between compression efficiency and visual quality. By feeding a QP map into the neural network, it can intelligently adjust the compression ratio based on the unique features of each image or video region. This enables the network to prioritize bit allocation, devoting more resources to critical areas like edges, textures, and faces, while assigning fewer bits to less important regions. The QP map can act as a guide for the neural network to identify and extract features that are resilient to compression-related distortions. By integrating the QP map into the feature extraction process, the network can learn to emphasize features that are less susceptible to QP-induced artifacts, ultimately leading to more reliable and accurate representations of the image or video content. By integrating the QP map into the neural network design, it can develop strategies to mitigate compression artifacts tied to specific QP values. For instance, the network can learn to minimize blocking effects in areas with high compression (i.e., high QP) or alleviate ringing effects in regions with low compression (i.e., low QP), resulting in a more visually pleasing and artifact-free output. By leveraging the QP map, the neural network can acquire a compression-agnostic understanding of the image or video, where the representation of the content is decoupled from the specific QP value used. This enables the network to produce more reliable and consistent results, unaffected by variations in compression settings, and ultimately leading to improved performance and accuracy.  
The QP map is input into a neural network that has the same spatial dimensions as the compressed input image, allowing the network to process the compression information in a spatially corresponding manner. QP value can be varied at frame level, slice level, or block level. However, in our proposed solution, we deal with the base QP value Base QP which indicates the primary QP value (i.e., 22, 27, 32, etc.) set during the encoding  
Figure 5: Illustration of down-sampling process of neural network output from YUV 4:4:4 to YUV 4:2:0 format.  
decoding process. The QP map produces a value that is scaled to a standard range, as specified by Equation (1).  
\[\text{QP}_{\text{map}}(\text{x},\text{y})=\frac{\text{QP}(\text{x},\text{y})}{ \text{QP}_{\text{max}}} \tag{1}\]  
where x represents the horizontal pixel coordinates, y represents the vertical pixel coordinates, and the maximum quantization parameter \(\text{QP}_{\text{max}}\) is set to 63. Here, x ranges from 1 to W (i.e., Width) and y ranges from 1 to H (i.e., Height). In the context of VVC, \(\text{QP}_{\text{max}}\) determines the maximum level of compression that can be applied to each coding unit within a frame.