Support for the efficient coding account of visual discomfort

Abstract

Sparse coding theories suggest that the visual brain is optimised to encode natural visual stimuli to minimise metabolic cost. It is thought that images that do not have the same statistical properties of natural images are unable to be coded efficiently and result in visual discomfort. Conversely, artworks are thought to be even more efficiently processed compared to natural images and so are aesthetically pleasing. This project investigated visual discomfort in uncomfortable images, natural scenes and artworks using a combination of low-level image statistical analysis, mathematical modelling and EEG measures. Results showed that the model response predicted discomfort judgements. Moreover, low-level image statistics including edge predictability predict discomfort judgements, whereas contrast information predicts the SSVEP responses. In conclusion, this study demonstrates that discomfort judgements for a wide set of images can be influenced by contrast and edge information, and can be predicted by our models of low-level vision, whilst neural responses are more defined by contrast-based metrics, when contrast is allowed to vary.