NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

TL;DR

This paper reviews the NTIRE 2022 challenge on improving compressed video quality through super-resolution and enhancement, introducing a new dataset and analyzing the state-of-the-art methods across three challenge tracks.

Contribution

It introduces the LDV 2.0 dataset for compressed video enhancement and provides a comprehensive evaluation of recent methods in a large-scale challenge setting.

Findings

State-of-the-art methods achieved significant quality improvements.

The LDV 2.0 dataset facilitates future research.

Multiple teams demonstrated competitive super-resolution and enhancement results.

Abstract

This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the LDV dataset (240 videos) and 95 additional videos. This challenge includes three tracks. Track 1 aims at enhancing the videos compressed by HEVC at a fixed QP. Track 2 and Track 3 target both the super-resolution and quality enhancement of HEVC compressed video. They require x2 and x4 super-resolution, respectively. The three tracks totally attract more than 600 registrations. In the test phase, 8 teams, 8 teams and 12 teams submitted the final results to Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of super-resolution and quality enhancement of compressed video. The proposed LDV 2.0 dataset is available at https://github.com/RenYang-home/LDV_dataset. The homepage of this challenge (including open-sourced codes) is at https://github.com/RenYang-home/NTIRE22_VEnh_SR.