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Bibliographic Details
Main Authors:	Li, Xin, Xu, Daoli, Luo, Wei, Xiang, Guoqiang, Li, Haoran, Zhuang, Chengyu, Chen, Zhibo, Guan, Jian, Li, Weping, Zhang, Weixia, Sun, Wei, Wang, Zhihua, Zhu, Dandan, Zhu, Chengguang, Gupta, Ayush, Agarwal, Rachit, Das, Shouvik, Das, Biplab Ch, Ghosh, Amartya, Fan, Kanglong, Wen, Wen, Zhai, Shuyan, Zhi, Tianwu, Zhang, Aoxiang, Liu, Jianzhao, Zhang, Yabin, Wang, Jiajun, Sun, Yipeng, Lian, Kaiwei, Yin, Banghao
Format:	Preprint
Published:	2026
Subjects:	Computer Vision and Pattern Recognition
Online Access:	https://arxiv.org/abs/2604.11207
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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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