A visual attention model for adapting images on small displays

Image adaptation, one of the essential problems in adaptive content delivery for universal access, has been actively explored for some time. Most existing approaches have focused on generic adaptation towards saving file size under constraints in client environment and hardly paid attention to user’s perception on the adapted result. Meanwhile, the major limitation on the user’s delivery context is moving from data volume (or time-to-wait) to screen size because of the galloping development of hardware technologies. In this paper, we propose a novel method for adapting images based on user attention. A generic and extensible image attention model is introduced based on three attributes (region of interest, attention value, and minimal perceptible size) associated with each attention object and a set of automatic modeling methods are presented to support this approach. A branch and bound algorithm is also developed to find the optimal adaptation efficiently. Experimental results demonstrate the usefulness of the proposed scheme and its potential application in the future.

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InstitutionMicrosoft Research
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