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At this stage, most Web content is intended for consumption on powerful desktop computers, and has to be adapted to meet the resource constraints that are typical of mobile devices. The main challenge for automatic content adaptation services lies in determining the best way to adapt content while minimizing user and content producer intervention. Our research puts forward Community-Driven Adaptation (CDA) that improves the experience of users browsing the Web on mobile devices by adapting content based on the data's relevance to the user's tasks.
CDA relies on the hypothesis that users can be automatically grouped into communities based on some common characteristics (e.g., type of mobile device, location, preferences) so that adapted content that is suitable for some members of the community is likely to be acceptable to other members as well. We started our early investigation on adaptation to web images.
The system consisted of two parts: a predictive transformation proxy and a group of adaptation applications (such as browser plug-ins) that interact with users. The User study involved some of the very popular web-applications such as online map systems and electronic commerce. Based on the community response, a tracer program residing at the proxy side will analyze all collected data and predict the optimal fidelity level at which these images should be presented.
CDA diagram 
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