Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Survey
It addresses the problem of automated quality assessment for portrait photos for researchers and practitioners, but is incremental as it surveys existing challenge solutions.
The paper reviews the NTIRE 2024 Portrait Quality Assessment Challenge, which aimed to develop deep neural networks for estimating perceptual quality in real portrait photos under diverse conditions, with 35 submissions and the top 5 results analyzed to gauge state-of-the-art performance.
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.