# Versatile Video Coding-Post Processing Feature Fusion:
## 3 Proposed Method
### Proposed Network Architecture  
The diagram in Figure 6 illustrates the proposed network architecture, which is designed to enhance the quality of reconstructed frames. The network takes as input the reconstructed frames and the corresponding QP map information. The network uses a QP map that is the same size as the reconstructed frame, and then concatenates the two before feeding them into the network for processing. The QP map supplies the network with prior knowledge about the frame quality variations caused by the QP used during compression, providing insight into the quality of the reconstructed frames when combined with the images. The concatenated QP map and reconstructed frame are then fed into the first convolutional layer, which applies a \(1\times 1\) kernel with 128 output channels to process the input. The \(1\times 1\) convolution kernel compress feature map reduces their number and dimensionality, while preserving crucial information. The 128-channel configuration strikes a balance between feature extraction and computational efficiency, enabling the model to learn a robust representation of the input data without incurring excessive computational costs. The parametric rectified linear unit (PreLU) activation function, applied after the initial convolutional layer, learns to tailor its rectification parameters to the input data, resulting in improved model performance. The resulting feature maps are subsequently passed into the feature extraction block for further processing. The network can use multiple feature extraction blocks to balance complexity and performance. We used 16 blocks in our experiments to optimize feature extraction while keeping the network simple and efficient. Every feature extraction block consists of a convolutional layer featuring a \(3\times 3\) kernel, 128 output channels, and a PReLU activation function. The \(3\times 3\) kernel size enables it to effectively identify small-scale features such as edges, lines, and textures, while also reducing the computational load, thereby speeding up training and inference processes. PReLU was used to aid convergence in deeper network layers, as it allows for varying degrees of nonlinearity, which is particularly important in deeper layers where nonlinearity tends to increase. A hierarchical feature fusion approach is employed across 16 feature extraction blocks. In each block, features from previous blocks are refined through a \(1\times 1\) convolutional layer,  
Figure 6: Architecture of the proposed CNN-based post-filtering method, integrating multiple feature extractions for enhanced output refinement. which learns to capture complex patterns. The refined features are then combined with the previous features to produce the final output of each block. This process is repeated, with each block aggregating features from its predecessors. Ultimately, the first block receives the collective output from all 16 blocks, resulting in a rich and integrated feature representation. The feature fusion process is mathematically represented by Equations (2) and (3), which describe how the features are combined and integrated to produce the final output. \[P^{\prime}{}_{N}=F_{conv1\times 1}\ (P_{N}) \tag{2}\]  
\[P^{\prime}{}_{N-i}=P^{\prime}{}_{N}+\sum_{i=1}^{15}F_{conv1\times 1}(P_{N-i}) \tag{3}\]  
where \(N\) represents the number of feature extraction blocks, \(P_{N}\) represents the convolution and activation function of each feature extraction blocks, \(P_{N-i}\) represents each preceding block where \(i\) ranges from 1 to 15, and \(P^{\prime}{}_{N-i}\) is the result of combining the features from the current block with those from the previous blocks, using a function \(F_{conv1\times 1}\) that represents the feature extraction process through a \(1\times 1\) convolution layer. The final feature from the first block is then fed into a convolutional layer with a \(3\times 3\) kernel size, which performs localized feature aggregation.