Journal Article

Fast-MFQE: A Fast Approach for Multi-Frame Quality Enhancement on Compressed Video

Zeng, Huanqiang, Shen, Xueyuan, Chen, Kemi, Chen, Jing

2023

Overview

For compressed images and videos, quality enhancement is essential. Though there have been remarkable achievements related to deep learning, deep learning models are too large to apply to real-time tasks. Therefore, a fast multi-frame quality enhancement method for compressed video, named Fast-MFQE, is proposed to meet the requirement of video-quality enhancement for real-time applications. There are three main modules in this method. One is the image pre-processing building module (IPPB), which is used to reduce redundant information of input images. The second one is the spatio-temporal fusion attention (STFA) module. It is introduced to effectively merge temporal and spatial information of input video frames. The third one is the feature reconstruction network (FRN), which is developed to effectively reconstruct and enhance the spatio-temporal information. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods in terms of lightweight parameters, inference speed, and quality enhancement performance. Even at a resolution of 1080p, the Fast-MFQE achieves a remarkable inference speed of over 25 frames per second, while providing a PSNR increase of 19.6% on average when QP = 37.

• Publisher: MDPI AG,MDPI
• Subject: Bandwidths / Coding standards / compressed video enhancement / inference time / lightweight models / Methods / Neural networks / real-time / Video compression