Visual stream connectivity predicts assessments of image quality
This work addresses the challenge of explaining perceptual similarity for vision science, but it is incremental as it builds on known biological mechanisms.
The authors tackled the problem of predicting human judgments of image similarity by deriving a formalization from early vision connectivity patterns, which outperformed standard measures of perceived image fidelity.
Some biological mechanisms of early vision are comparatively well understood, but they have yet to be evaluated for their ability to accurately predict and explain human judgments of image similarity. From well-studied simple connectivity patterns in early vision, we derive a novel formalization of the psychophysics of similarity, showing the differential geometry that provides accurate and explanatory accounts of perceptual similarity judgments. These predictions then are further improved via simple regression on human behavioral reports, which in turn are used to construct more elaborate hypothesized neural connectivity patterns. Both approaches outperform standard successful measures of perceived image fidelity from the literature, as well as providing explanatory principles of similarity perception.