# Preprocessing for Multi-Dimensional Enhancement and Reconstruction in Neural Video Compression
## 4 Results and Discussion
### Discussion  
The results of this study underscore the significant advancements made by the MDER preprocessing method in the realm of neural video encoders. Previous research has either substituted traditional video encoder modules with neural networks or developed entirely neural-based models. However, these approaches often struggle to fully leverage video data redundancies and address noise interference. Our MDER method allows the neural encoder to learn the essential features required for coding, which enhances and reconstructs important features within the video data. The experimental results show that the reduction in Bpp translates directly to more efficient video compression, which is crucial for reducing bandwidth consumption and storage requirements. This improvement is particularly beneficial in applications such as video streaming, where bandwidth efficiency is paramount, and in storage solutions, where reducing file size can lead to significant cost savings. Specifically, the MDER preprocessing module enhances video encoding by utilizing residual learning and feature distillation techniques. This approach reduces data redundancy while preserving high-frequency details, which are crucial for maintaining visual fidelity. Additionally, the unique dense connectivity properties of Dense Blocks ensure direct connections between each layer and all preceding layers. This enhances the representation capability of the current layer and contributes to improved compression efficiency. This approach significantly reduces redundant data and enhances the stability and reliability of the features by removing high-frequency noise through the preprocessing module. Dense Blocks provide each network layer with access to all preceding layers' features through dense connections in order to enhance feature extraction. This approach allows for more comprehensive capture and utilization of feature information. Compared to DPP's dilated convolutions and RPP's residual networks, MDER more thoroughly extracts and enhances important features, which improves feature richness and accuracy. While DPP utilizes dilated convolutions to expand the receptive field, and RPP employs residual connections to handle high-frequency details, neither method adequately filters redundant video data. As shown in Figure 6, deploying these preprocessing methods before DFVC results in enhanced encoding efficiency, and our preprocessing method demonstrates significant advantages in improving video encoding efficiency while maintaining high-quality video output. Although an identical metric is not available to objectively quantify the computational complexity of preprocessing modules, a subjective comparison shows that employing knowledge distillation to compress the network scale in MDFRNet and substituting traditional convolution layers with SCconv for feature refinement results in a more lightweight network compared to DPP, which utilizes dilated convolutions to expand the receptive field, and RPP, which incorporates residual connections to handle high-frequency details. While DPP and RPP are effective, their designs do not fully filter out redundant video data and are less lightweight, which makes them more challenging to deploy on CPU-based devices. Table 1 and Figure 6 illustrate that MDER outperforms DPP and RPP in terms of processing speed and efficiency. Specifically, MDER achieves an inference performance of 1080p@49 FPS on a single NVIDIA GeForce RTX 2080 Ti, which is superior to the FPS achieved by DPP and RPP. This performance advantage is particularly relevant in scenarios such as live streaming and video conferencing, where real-time processing with high visual fidelity is essential. The lower computational complexity of MDER also suggests that it can be more easily deployed on devices with limited processing power, further expanding its applicability. The results of this study have several important implications.