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Image quality assessment (IQA) is an important task of image processing and has diverse applications, such as image super-resolution reconstruction, image transmission and monitoring systems. This paper proposes a perceptual hashing algorithm with complementary color wavelet transform (CCWT) and compressed sensing (CS) for reduced-reference (RR) IQA. The CCWT is exploited to decompose input color image into different sub-bands. Since the calculation of CCWT uses all color channels without discarding any information, the distortions introduced by digital operations on color channels are preserved in the CCWT sub-bands. The block-based CS is used to extract features from the CCWT sub-bands. As the Euclidean distance between the block-based CS features is slightly influenced by content-preserving operations, perceptual features constructed by Euclidean distances are robust, discriminative and compact. Hash sequence is finally determined by quantifying the perceptual features. Effectiveness of the proposed hashing is verified by various experiments on four open image databases. Experimental results demonstrate that the proposed hashing is superior to some state-of-the-art algorithms in terms of classification and RR IQA application. [ABSTRACT FROM AUTHOR]

Recently, multiple ($k, n$) region-incrementing VCSs (RIVCSs) have been proposed that can gradually reconstruct secrets in a single image. In ($k, n$)-RIVCS, the secret image is subdivided into multiple regions in such a way that any $t$ shadow images, where $k \le t \le n$ , can be used to reveal the secret in the ($t-k+1$)th region. This region-incrementing property provides progressive decoding. However, the regions in previous RIVCSs are disjointed: no two regions have overlapping areas. In this paper, we discuss a ($k, n$) region-in-region progressive VCS (RPVCS). Our ($k, n$)-RPVCS has ($n-k+1$) secrecy-level regions, and each region can be located within any previous region. This new type of region allocation not only provides more areas in which to hide the secret compared with the non-overlapping regions in RIVCS but also provides different presentation methods in progressive decoding. [ABSTRACT FROM AUTHOR]

The screen content images (SCIs) quality influences the user experience and the interactive performance of remote computing systems. With numerous approaches proposed to evaluate the quality of natural images, much less work has been dedicated to reduced-reference image quality assessment (RR-IQA) of SCIs. Here, we propose an RR-IQA method from the perspective of SCI visual perception. In particular, the quality of the distorted SCI is evaluated by comparing a set of extracted statistical features that consider both primary visual information and unpredictable uncertainty. A unique property that differentiates the proposed method from previous RR-IQA methods for natural images is the consideration of behaviors when human subjects view the screen content, which motivates us to establish the perceptual model according to the distinct properties of SCIs. Validations based on the screen content IQA database show that the proposed algorithm provides accurate predictions across a wide range of SCI distortions with negligible transmission overhead. [ABSTRACT FROM PUBLISHER]

Given a secret image S , a set \mathscr {P} of n participants and a strong access structure ( \Gamma \mathrm {\mathbf {Qual}} , \Gamma \mathrm {\mathbf {Forb}}) , a visual cryptographic scheme (VCS) for general access structures (GVCS) encodes S into n shares of transparencies such that the participants of each qualified set in \Gamma \mathrm {\mathbf {Qual}} can reveal S by superimposing their shares; whereas those of any forbidden set in \Gamma \mathrm {\mathbf {Forb}} obtain nothing about $S$ . Elegant GVCS constructions have been designed with smaller pixel expansions. Yet, whether the pixel expansion derived is minimized is still unknown. In this research, we generalize and extend our recent study, in which the modeling of minimizing the pixel expansion for a ( $k$ , $n$ )-VCS into an integer linear program (ILP) was proposed, to ensure that the constraints for GVCS can be satisfied. The pixel expansion of a GVCS can thus be minimized by solving the corresponding ILP. This is the first result in the literature for acquiring the optimal solution to a GVCS. The computational study demonstrates the effectiveness of our ILP and also verifies that the best solution from previous GVCS approaches is optimal for all strong access structures of $n \le 4$ ; but no more reliable for those of $n\ge 5$ . [ABSTRACT FROM PUBLISHER]

This paper re-examines the problem of visual secret sharing for general access structures by using visual cryptograms of random grids (VCRG). Given a binary or color secret image shared by a set of n participants with a strong access structure, we devise two effective algorithms to produce a set of VCRG so that the members in each qualified set can reconstruct the secret image by superimposing their shares, while those in any forbidden set cannot. Our algorithms do not require any extra pixel expansion, which is indispensable and grows exponentially as n increases in conventional visual cryptographic schemes. The feasibility, light contrasts, flexibility, and limitations of our algorithms are explored from both theoretical and empirical points of view. [ABSTRACT FROM PUBLISHER]

Recently, Wang introduced a novel (2, n) region incrementing visual cryptographic scheme (RIVCS), which can gradually reconstruct secrets in a single image with multiple security levels. In RIVCS, the secret image is subdivided into multiple regions in such a way that any t shadow images, where 2\leq t\leq n, can be used to reveal the (t-1)th region. However, Wang's scheme suffers from the incorrect-color problem, which the colors of reconstructed images may be reversed (i.e., the black and white are reversed). If the color of text is also the secret information, the incorrect-color problem will compromise the secret. Additionally, Wang's scheme is only suitable for the 2-out-of-n case, i.e., (k,n)-RIVCS where k=2. In this paper, we propose a general (k,n)-RIVCS, where k and n are any integers, that is able to reveal correct colors of all regions. This paper has made three main contributions: 1) our scheme is a general (k,n)-RIVCS, where k and n can be any integers; 2) the incorrect-color problem is solved; and 3) our (k,n)-RIVCS is theoretically proven to satisfy the security and contrast conditions. [ABSTRACT FROM PUBLISHER]