Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Survey
Nicolas Chahine Marcos V. Conde Daniela Carfora Gabriel Pacianotto Benoit Pochon Sira Ferradans Radu Timofte
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.