Current state-of-the-art algorithms that process images for human use treat images as signals; these techniques are successful because of both advanced signal processing techniques and signal-processing based human visual system (HVS) models. Many applications, however are better approached with a higher-level view of images, considering color, structure, or even object content. No unified model of either images or the HVS processing exists at this higher-level view; such a model can substantially advance the current state-of-the-art in image processing. This research involves developing such a model and incorporating it into practical algorithms such as low-rate compression, low-rate facial compression allowing recognition, and medical imaging.
The human visual system initially performs low-level signal analysis and ultimately ends in cognition. Many models of the signal analysis stage which predict responses to simple stimuli, but these models fail for natural images because humans' higher-order processing of structure produces cognitive effects which cannot be modeled using only responses to simple stimuli. Cognition is thought to gradually occur during input processing; no single portion of the HVS is singularly responsible for recognition or transformation of the internal representation to an abstract concept. The continuum of visual processing from signal analysis to cognition suggests that signal-processing-based modeling of the HVS can be extended to include some structural processing. This research develops such models through three psychophysical experiments. The first is a controlled masking study allowing development of a visual masking model incorporating structure. The second rates observers' willingness to accept distortions, relating acceptance with detection results from the first study. The third compares recognition times for images represented using structural and signal-based representations, quantifying the use of structure in cognitive tasks.
