# MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video

## TL;DR

This paper introduces a novel multi-frame quality enhancement method for compressed videos that leverages frame similarity and deep learning to improve video quality, outperforming existing single-frame approaches.

## Contribution

It proposes the first multi-frame approach using BiLSTM for PQF detection and a specialized CNN for quality enhancement, utilizing neighboring high-quality frames for better results.

## Key findings

- Effective in enhancing compressed video quality
- Outperforms state-of-the-art single-frame methods
- Demonstrates strong generalization across videos

## Abstract

The past few years have witnessed great success in applying deep learning to enhance the quality of compressed image/video. The existing approaches mainly focus on enhancing the quality of a single frame, not considering the similarity between consecutive frames. Since heavy fluctuation exists across compressed video frames as investigated in this paper, frame similarity can be utilized for quality enhancement of low-quality frames given their neighboring high-quality frames. This task is Multi-Frame Quality Enhancement (MFQE). Accordingly, this paper proposes an MFQE approach for compressed video, as the first attempt in this direction. In our approach, we firstly develop a Bidirectional Long Short-Term Memory (BiLSTM) based detector to locate Peak Quality Frames (PQFs) in compressed video. Then, a novel Multi-Frame Convolutional Neural Network (MF-CNN) is designed to enhance the quality of compressed video, in which the non-PQF and its nearest two PQFs are the input. In MF-CNN, motion between the non-PQF and PQFs is compensated by a motion compensation subnet. Subsequently, a quality enhancement subnet fuses the non-PQF and compensated PQFs, and then reduces the compression artifacts of the non-PQF. Also, PQF quality is enhanced in the same way. Finally, experiments validate the effectiveness and generalization ability of our MFQE approach in advancing the state-of-the-art quality enhancement of compressed video. The code is available at https://github.com/RyanXingQL/MFQEv2.0.git.