Public auditing enables efficient integrity checks of data assigned to cloud servers. In this article, we revisit the public auditing for encrypted data, in which a major concern is how to effectively support data dynamics, i.e., data modification, insertion, and deletion. We first determine which factor in existing auditing schemes most limits data dynamics from a cost perspective. We then propose a novel public auditing scheme that provides data dynamics that are orders of magnitude faster than previous methods. Our auditing challenge-response protocol reduces the computation cost of the third-party auditor (TPA) significantly, thus increasing the verification speed for the auditing results. Performance and security analysis demonstrates that the proposed scheme generates minimal computation costs while guaranteeing data integrity and privacy against an untrusted cloud.

In this letter, we propose the objective metric that can precisely predict the perceptual quality of contrast-changed images using inter-pixel contextual information. The metric consists of two parts. One is a two-dimensional (2-D) histogram-based contrast quality measure that utilizes the distribution of the gray-level differences between adjacent pixels. We design the desired 2-D histogram considering the characteristic of an adequately high contrast image and predict contrast quality by comparing the desired 2-D histogram with 2-D histograms of an original image and a contrast-changed image. The other is a spatial entropy based one that uses the information of spatial location distribution of gray-levels. A comparison is carried out with many IQA metrics on five contrast related databases. Experimental results show that the proposed metric provides a more accurate prediction of human perception of contrast change than other metrics.

A novel image enhancement method for infrared (IR) images is presented. The proposed method consists of two parts considering the characteristics of high-dynamic-range IR images. First, we attempt to enhance image contrast by introducing the ramp distribution that increases with a constant slope in an ordered histogram domain. The ramp-distributed histogram is incorporated into an optimization problem with a sorted histogram of the input image to calculate a modified histogram. Second, to deal with blurred effects on IR images, we propose a relative edge-strength index for high-boost filtering to effectively suppress noise in relatively uniform regions. Compared with various conventional and state-of-the-art algorithms, the proposed method shows highly competitive performance.

This letter presents an adaptive contrast enhancement algorithm considering both preservation of the shape of a one-dimensional (1-D) histogram and statistical information on the gray-level differences between neighboring pixels obtained by a 2-D histogram. The proposed system consists of two modules. One is to enhance the entire contrast by stretching the 1-D histogram while preserving the shape of the histogram. The other is to improve the details of nonsmooth areas occurring frequently in input images. These are formulated into a single constrained optimization problem. Compared with several state-of-the-art enhancement algorithms, the proposed algorithm shows highly competitive performance.