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.
Abstract (translated)
本文回顾了NTIRE 2024 肖像质量评估挑战，重点关注所提出的解决方案和结果。这个挑战的目标是获得一个高效的深度神经网络，能够估计真实肖像照片的感知质量。为了适应各种场景和照明条件（室内、户外、低光），运动、模糊和其他具有挑战性的条件，方法必须具有泛化性。在挑战期间，共有140名参与者注册，35名提交了结果。对前五名提交作品的性能进行了审查，并提供了一个评估当前面部质量评估技术的指标。
URL
https://arxiv.org/abs/2404.11159
