.png){kind=icon}
.png){kind=icon}
Return
DCMR: Degradation compensation and multi-dimensional reconstruction based pre-processing for video coding
.png){kind=icon}
PRE
AI
DOI：10.1016/j.displa.2024.102866.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The rapid growth of video data poses a serious challenge to the limited bandwidth. Video coding preprocessing technology can remove coding noise without changing the architecture of the codec. Therefore, it can improve the coding efficiency while ensuring a high degree of compatibility with existing codec. However, the existing pre-processing methods have the problem of feature redundancy, and lack an effective mechanism to recover high-frequency details. In view of these problems, we propose a Degradation Compensation and Multi-dimensional Reconstruction (DCMR) pre-processing method for video coding to improve compression efficiency. Firstly, we develop a degradation compensation model, which aims at filtering the coding noise in the original video and relieving the frame quality degradation caused by transmission. Secondly, we construct a lightweight multi-dimensional feature reconstruction network, which combines residual learning and feature distillation. It aims to enhance and refine the key features related to coding from both spatial and channel dimensions while suppressing irrelevant features. In addition, we design a weighted guided image filter detail enhancement convolution module, which is specifically used to recover the high-frequency details lost in the denoising process. Finally, we introduce an adaptive discrete cosine transform loss to balance coding efficiency and quality. Experimental results demonstrate that compared with the original codec H.266/VVC, the proposed DCMR can achieve BD-rate (VMAF) and BD-rate (MOS) gains by 21.62% and 12.99% respectively on VVC, UVG, and MCL-JCV datasets.
Keywords:
Video coding
Pre-processing
High-frequency information
Detail enhancement
H.266/VVC
Journal
IF:
3.4
Papers:
2.2K
Citations:
3.2K