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.
@article{arxiv.2604.11207,
title = {LoViF 2026 Challenge on Human-oriented Semantic Image Quality Assessment: Methods and Results},
author = {Xin Li and Daoli Xu and Wei Luo and Guoqiang Xiang and Haoran Li and Chengyu Zhuang and Zhibo Chen and Jian Guan and Weping Li and Weixia Zhang and Wei Sun and Zhihua Wang and Dandan Zhu and Chengguang Zhu and Ayush Gupta and Rachit Agarwal and Shouvik Das and Biplab Ch Das and Amartya Ghosh and Kanglong Fan and Wen Wen and Shuyan Zhai and Tianwu Zhi and Aoxiang Zhang and Jianzhao Liu and Yabin Zhang and Jiajun Wang and Yipeng Sun and Kaiwei Lian and Banghao Yin},
journal= {arXiv preprint arXiv:2604.11207},
year = {2026}
}