Neurally informed assessment of perceived natural texture image quality

Conventionally, the quality of images and related codecs are assessed using subjective tests, such as Degradation Category Rating. These quality assessments consider the behavioral level only. Recently, it has been proposed to complement this approach by investigating how quality is processed in the brain of a user (using electroencephalography, EEG), potentially leading to results that are less biased by subjective factors. In this paper, a novel method is presented for assessing how image quality is processed on a neural level, using Steady-State Visual Evoked Potentials (SSVEPs) as EEG features. We tested our approach in an EEG study with 16 participants who were presented with distorted images of natural textures. Subsequently, we compared our approach analogously to the standardized Degradation Category Rating quality assessment. Remarkably, our novel method yields a correlation of |r| = 0.93 to MOS on the recorded dataset.