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This paper introduces an effective processing framework nominated ICP (ImageCloud Processing) to powerfully cope with the data explosion in imageprocessing field. While most previous researches focus on optimizing the imageprocessing algorithms to gain higher efficiency, our work dedicates toproviding a general framework for those image processing algorithms, which canbe implemented in parallel so as to achieve a boost in time efficiency withoutcompromising the results performance along with the increasing image scale. Theproposed ICP framework consists of two mechanisms, i.e. SICP (Static ICP) andDICP (Dynamic ICP). Specifically, SICP is aimed at processing the big imagedata pre-stored in the distributed system, while DICP is proposed for dynamicinput. To accomplish SICP, two novel data representations named P-Image andBig-Image are designed to cooperate with MapReduce to achieve more optimizedconfiguration and higher efficiency. DICP is implemented through a parallelprocessing procedure working with the traditional processing mechanism of thedistributed system. Representative results of comprehensive experiments on thechallenging ImageNet dataset are selected to validate the capacity of ourproposed ICP framework over the traditional state-of-the-art methods, both intime efficiency and quality of results.

A Hierarchical Distributed Processing Framework for Big Image Data