# Preprocessing for Multi-Dimensional Enhancement and Reconstruction in Neural Video Compression
## 2 Materials and Methods
### Multi-Dimensional Enhancement and Reconstruction (MDER)  
The MDER preprocessing method, as illustrated in Figure 2, is designed to extract and enhance essential video image features across multiple dimensions for the subsequent encoding process. Firstly, the Degradation Compensation Model (DCM) is applied to eliminate coding noise and restore degradations such as blurring, scaling, and lossy compression that may occur during transmission. Next, a lightweight fully convolutional neural network, MDFRNet, is utilized. This network integrates residual learning and feature distillation principles [15] to extract and reconstruct key coding features from both spatial and channel dimensions [16]. After preprocessing video data with the DCM, MDFRNet is better guided to focus on the core features, accelerating network convergence and resulting in more representative feature representations that enhance encoding performance. For each frame to be encoded, it is first passed through the DCM to remove encoding noise and restore image degradation, which improves the efficiency and accuracy of feature extraction. Subsequently, the processed data are forward-propagated through the trained MDFRNet, where encoding-relevant features are extracted and reconstructed, which results in the preprocessed output image. However, the denoising process may lead to the loss of high-frequency details such as edges, which are critical to maintaining video quality. To mitigate this issue, we incorporate a sharpening filter to restore high-frequency details and introduce an Adaptive Discrete Cosine Transform (ADCT) loss function. This loss function specifically optimizes the retention of different types of frequency information during neural network training. Recognizing that the separated high-frequency components also contain critical details for human visual perception, we designed a specialized adaptive adjustment strategy to preserve these important high-frequency details. This significantly reduces the volume of video data, which improves video encoding efficiency and performance without compromising image quality. Additionally, the MAE loss function is employed to ensure that the network possesses fundamental image reconstruction capabilities, and the full-reference image quality evaluation metric MS-SSIM is also integrated into the loss function. By combining these various preprocessing techniques, we have improved the coding efficiency of neural video encoders without sacrificing visual quality, contributing significant technological innovations to the field of video coding. **DCM. ** The model comprises four parts: the Blur Degradation module, Noise Degradation module, Resize Degradation module, and JPEG Degradation module, each addressing specific types of video degradation. Through the deployment of DCM, we effectively remove high-frequency noise and degradation during transmission by combining different types of filters to enhance image quality. This process provides high-quality input features to MDFRNet, which accelerates network convergence and enhances compression efficiency. By applying the DCM, the quality of the input frame is reduced, and the amount of high-frequency information is greatly reduced, which helps to remove high-frequency noise. At the same time, the overall content and structure of the frame are highlighted. This strategy can make MDFRNet pay more attention to the intrinsic features in the training phase. **MDFRNet. ** First, a desub-pixel convolution layer [17] is introduced to downsample the input features and increase channel depth, which is utilized to enhance the network's capacity to capture intricate video details. Subsequently, multiple Residual Local Feature Blocks (RLFBs) are cascaded to extract detailed features [18]. Each RLFB employs a modest number of stacked convolution (CONV) and rectified linear unit (ReLU) layers for local feature extraction. Specifically, each feature refinement module in RLFB contains a single 3 \(\times\) 3 convolution layer followed by a ReLU activation function layer.