mlsanityData to 14 Oct 2026

AIM 2024 Challenge on Video Super-Resolution Quality Assessment: Methods and Results

arXiv 2410.04225 ↗First posted 5 Oct 2024eess.IV cs.CV cs.MMcited by 1

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Abstract (arXiv)

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

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 39 of its 58 references that are in the map. First posted on arXiv 5 Oct 2024.

Builds on

39 of 58 references.

References · 39

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27 more
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Citing papers per year: 2026: 1.

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  1. 2026How Accurate are Video Quality Models for Diffusion-Based Video Super-Resolution?cited by 1