Quantifying the Use of Structure in Cognition

Modern algorithms that process images to be viewed by humans analyze the images strictly as signals, where processing is typically limited to the pixel and frequency domains. The continuum of visual processing by the human visual system (HVS) from signal analysis to cognition indicates that the signal-processing based model of the HVS could be extended to include some higher-level, structural processing. A preliminary experiment was conducted to study the relative importance of structural (higher-level) and signal-based (lower-level) representations of natural images in a cognitive task. The results from this experiment suggest that signalbased representations are only meaningful to human observers when the proportion of high frequency content, which conveys structural information, exceeds a seemingly fixed proportion. This work investigates the results of this experiment towards the development of a rudimentary measure of visual entropy.