# Preprocessing for Multi-Dimensional Enhancement and Reconstruction in Neural Video Compression
## 4 Results and Discussion  
### Results  
To verify the effectiveness of the MDER method when used with neural video encoders, we deployed it as shown in Figure 1 and compared the experimental results before and after MDER deployment. Class B-D YUV420P sequences of VVC were tested. The rate-distortion (RD) curves for each category are presented in Figure 5. During inference, the H.266 encoder generated I-frames with Constant Rate Factor (CRF) values of 20, 23, 26, and 29. These I-frames served as reference frames for each GOP in DFVC, where the GOPSIZE was set to 12.  
As shown in Figure 5, the modified DFVC achieved a reduction in Bpp by 0.0522 and 0.0602 under the same PSNR and MS-SSIM conditions, respectively, compared to the original FVC. This indicates the advantages of using Dense Block over Residual Block. This is especially evident in feature extraction, reconstruction, and compressing motion and residual information. These improvements positively enhance coding efficiency. When MDER preprocessing was applied to both FVC and DFVC, Bpp was reduced by 0.0578 and 0.0714, respectively, under the same PSNR and MS-SSIM conditions. Whether deployed on the FVC or the enhanced DFVC, the integration of the MDER preprocessing method consistently achieves significant performance gains in compre  
Figure 5: The rate–distortion curves for the VVC Class B–D dataset on PSNR and MS-SSIM.  
video encoders. This highlights the effectiveness of our proposed MDER preprocessing method in improving compression efficiency.  
Additionally, a further comparison between MDER + FVC and MDER + DFVC showed that MDER + DFVC achieved savings of 0.0488 and 0.0415 Bpp, respectively, under identical PSNR and MS-SSIM conditions compared to MDER + FVC. This highlights that incorporating a preprocessing module and performing end-to-end optimization in the improved neural video encoder results in a more substantial improvement in compression efficiency than using a standalone enhanced encoder or merely adding a preprocessing module. The results in Figure 5 clearly demonstrate the substantial effectiveness of our MDER preprocessing method in enhancing the coding efficiency of neural video encoders while maintaining high-quality video output.  
To further validate the superiority of the proposed MDER method in neural video encoders, a comparative analysis was performed against other existing methods. We deployed MDER, DPP [14], and RPP [15] within the improved DFVC neural video encoder. Then, we plotted RD curves for these three methods to facilitate a comparative evaluation. The results show that, under equivalent PSNR values, Bpp was reduced by 0.0192 and 0.0133, respectively. Under equivalent MS-SSIM values, Bpp was reduced by 0.0377 and 0.0183, respectively. Additionally, under equivalent VMAF values, Bpp was reduced by an average of 0.049 and 0.045, respectively. Specifically, after incorporating the MDER preprocessing method before the DFVC, the experimental results show that, under equivalent PSNR, MS-SSIM, and VMAF values, the average Bpp was reduced by 0.0488, 0.0415, and 0.114, respectively. The results confirm that, while each preprocessing method enhances DFVC's encoding efficiency, the MDER method offers a significant enhancement in video coding efficiency while maintaining high-quality output, which demonstrates its clear advantages.