# Preprocessing for Multi-Dimensional Enhancement and Reconstruction in Neural Video Compression
## 1 Introduction
[12] built a Pre-Editing Network (PEN), which uses a ResNet cascade and a patch-based Spatial Transformer Network to smooth an image. [PERSON] et al. [13] proposed a concept of deep perceptual preprocessing (DPP), which employs expansion convolutions with varying expansion rates at each layer. This architecture enhances the receptive field to capture more comprehensive feature information and produce higher-quality, more stable coding features. [PERSON] et al. [14] introduced a rate perception optimized preprocessing (RPP) method for video coding, which integrates perceptual enhancement mechanisms to optimize video frames for both bit rate efficiency and perceptual quality. These methods leverage neural networks' self-learning capabilities to enhance compression efficiency. Despite these advancements, current learning-based approaches often fall short in adequately filtering redundant features and lack a robust mechanism to recover high-frequency details in video data. Furthermore, the existing network architectures are not sufficiently lightweight to be deployed to CPU-based end-user devices, which highlights the potential for further research in preprocessing techniques to enhance video coding efficiency. To address these challenges, we propose a Multi-Dimensional Enhancement and Reconstruction (MDER) preprocessing method aimed at optimizing video compression efficiency. First, we introduce a degradation compensation module to remove encoding noise from the original video and relieve the frame quality degradation caused by transmission, which improves the efficiency of feature extraction. Second, we construct a lightweight fully convolutional neural network with multi-dimensional deredundancy. This network utilizes residual learning and knowledge distillation to enhance and refine key features from both spatial and channel dimensions, which suppresses irrelevant features. In addition, we incorporate Dense Blocks during the feature extraction phase to maximize the utilization of redundant information, which makes the network more efficient in processing residual information and further enhancing and reconstructing important features. Finally, the preprocessed frames are converted from pixel space to feature space through the feature extraction module of Dense Feature-Enhanced Video Compression (DFVC), which enables more accurate motion estimation and motion compensation. Overall, this comprehensive preprocessing approach allows the DFVC neural video encoder to learn essential coding features more efficiently and improve the coding efficiency without compromising the visual quality. The contributions of this paper are summarized as follows:  
1. We propose a Multi-Dimensional Enhancement and Reconstruction (MDER) preprocessing method for video coding which effectively removes encoding noise and enhances details to reconstruct frames for video coding. 2. Dense Blocks are integrated to further maximize the utilization of redundant information, which can process and utilize the information more effectively and enhance the efficiency of residual information processing. 3. Our proposed method can improve coding efficiency while maintaining consistent visual quality and is more easily deployed on devices with limited processing power.