29支团队挑战视频超分辨率质量评估，5支胜出超越现有水平。
AIM 2024 Challenge on Video Super-Resolution Quality Assessment: Methods and Results
- 基于15万+次众包对比投票，构建大规模视频超分质量评估数据集。
- 5支参赛团队在2倍和4倍超分任务中均超越当前最佳方法。
- 首次公开完整测试集，推动视频超分辨率质量评估研究发展。
本文介绍作为ECCV 2024会议同期举办的图像操纵前沿研讨会（AIM）一部分的视频超分辨率质量评估（QA）挑战赛。该挑战旨在为现代图像与视频超分辨率算法生成的2倍与4倍上采样视频开发客观质量评估方法。评估基于超过15万次众包配对比较产生的主观评分，覆盖52种超分辨率方法及1124个上采样视频。目标是突破传统质量评估方法在该任务中应用受限的瓶颈，推动视频超分辨率质量评估技术进步。共有29支队伍注册参与，其中5支提交最终结果，全部表现优于当前最先进水平。所有数据，包括私有测试子集，均已公开于挑战主页：https://challenges.videoprocessing.ai/challenges/super-resolution-metrics-challenge.html。
原文摘要 · Abstract (English)
This paper presents the Video Super-Resolution (SR) Quality Assessment (QA) Challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2024. The task of this challenge was to develop an objective QA method for videos upscaled 2x and 4x by modern image- and video-SR algorithms. QA methods were evaluated by comparing their output with aggregate subjective scores collected from >150,000 pairwise votes obtained through crowd-sourced comparisons across 52 SR methods and 1124 upscaled videos. The goal was to advance the state-of-the-art in SR QA, which had proven to be a challenging problem with limited applicability of traditional QA methods. The challenge had 29 registered participants, and 5 teams had submitted their final results, all outperforming the current state-of-the-art. All data, including the private test subset, has been made publicly available on the challenge homepage at https://challenges.videoprocessing.ai/challenges/super-resolution-metrics-challenge.html
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