  This paper introduces an effective processing framework nominated ICP (Image
Cloud Processing) to powerfully cope with the data explosion in image
processing field. While most previous researches focus on optimizing the image
processing algorithms to gain higher efficiency, our work dedicates to
providing a general framework for those image processing algorithms, which can
be implemented in parallel so as to achieve a boost in time efficiency without
compromising the results performance along with the increasing image scale. The
proposed ICP framework consists of two mechanisms, i.e. SICP (Static ICP) and
DICP (Dynamic ICP). Specifically, SICP is aimed at processing the big image
data pre-stored in the distributed system, while DICP is proposed for dynamic
input. To accomplish SICP, two novel data representations named P-Image and
Big-Image are designed to cooperate with MapReduce to achieve more optimized
configuration and higher efficiency. DICP is implemented through a parallel
processing procedure working with the traditional processing mechanism of the
distributed system. Representative results of comprehensive experiments on the
challenging ImageNet dataset are selected to validate the capacity of our
proposed ICP framework over the traditional state-of-the-art methods, both in
time efficiency and quality of results.
