构建首个面向人类的语义图像质量评估数据集，推动语义感知图像优化发展。
LoViF 2026 Challenge on Human-oriented Semantic Image Quality Assessment: Methods and Results
- 构建包含510组训练、80组验证和160组测试的语义质量评估数据集
- 6支团队在挑战赛中取得当前最优性能，验证新基准有效性
- 适合关注语义感知图像处理与人因评价的研究者参考
本文回顾了2026年LoViF人类导向语义图像质量评估挑战赛。该挑战旨在探索从人类视角评估图像语义信息损失的新方向，推动语义编码、语义处理及语义优化等新兴领域的发展。不同于现有质量评估数据集，本研究构建了名为SeIQA的人类导向语义质量评估数据集，分为三部分：(i) 训练数据：510对退化图像及其对应的真实参考图像；(ii) 验证数据：80对退化图像及其真实参考图像；(iii) 测试数据：160对退化图像及其真实参考图像。本次挑战的核心目标是建立一个全新且强大的人类导向语义图像质量评估基准。共有58支队伍注册，其中6支提交了有效解决方案和结果报告，均在SeIQA数据集上达到当前最优（SOTA）表现。
原文摘要 · Abstract (English)
This paper reviews the LoViF 2026 Challenge on Human-oriented Semantic Image Quality Assessment. This challenge aims to raise a new direction, i.e., how to evaluate the loss of semantic information from the human perspective, intending to promote the development of some new directions, like semantic coding, processing, and semantic-oriented optimization, etc. Unlike existing datasets of quality assessment, we form a dataset of human-oriented semantic quality assessment, termed the SeIQA dataset. This dataset is divided into three parts for this competition: (i) training data: 510 pairs of degraded images and their corresponding ground truth references; (ii) validation data: 80 pairs of degraded images and their corresponding ground-truth references; (iii) testing data: 160 pairs of degraded images and their corresponding ground-truth references. The primary objective of this challenge is to establish a new and powerful benchmark for human-oriented semantic image quality assessment. There are a total of 58 teams registered in this competition, and 6 teams submitted valid solutions and fact sheets for the final testing phase. These submissions achieved state-of-the-art (SOTA) performance on the SeIQA dataset.
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