Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Survey
Nicolas Chahine, Marcos V. Conde, Daniela Carfora, Gabriel Pacianotto, Benoit Pochon, Sira Ferradans, Radu Timofte, Zhichao Duan, Xinrui Xu, Yipo Huang, Quan Yuan, Xiangfei Sheng, Zhichao Yang, Leida Li, Haotian Fan, Fangyuan Kong, Yifang Xu, Wei Sun, Weixia Zhang, Yanwei Jiang, Haoning Wu, Zicheng Zhang, Jun Jia, Yingjie Zhou, Zhongpeng Ji, Xiongkuo Min, Weisi Lin, Guangtao Zhai, Xiaoqi Wang, Junqi Liu, Zixi Guo, Yun Zhang, Zewen Chen, Wen Wang, Juan Wang, Bing Li, Zhichao Duan, Xinrui Xu, Yipo Huang, Quan Yuan, Xiangfei Sheng, Zhichao Yang, Leida Li, Haotian Fan, Fangyuan Kong, Yifang Xu, Wei Sun, Weixia Zhang, Yanwei Jiang, Haoning Wu, Zicheng Zhang, Jun Jia, Yingjie Zhou, Zhongpeng Ji, Xiongkuo Min, Weisi Lin, Guangtao Zhai, Zewen Chen, Wen Wang, Juan Wang, Bing Li, Xiaoqi Wang, Junqi Liu, Zixi Guo, Yun Zhang
Abstract
This paper reviews the NTIRE 2024 Portrait Quality Assessment Challenge, highlighting the proposed solutions and results. This challenge aims to obtain an efficient deep neural network capable of estimating the perceptual quality of real portrait photos. The methods must generalize to diverse scenes and diverse lighting conditions (indoor, outdoor, low-light), movement, blur, and other challenging conditions. In the challenge, 140 participants registered, and 35 submitted results during the challenge period. The performance of the top 5 submissions is reviewed and provided here as a gauge for the current state-of-the-art in Portrait Quality Assessment.