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Deep modal can provide supplementary features for RGB images, which deeply improves the performance of salient object detection (SOD). However, depth images are disturbed by external factors during the acquisition process, resulting in low-quality acquisitions. Moreover, there are differences between the RGB and depth modals, so simply fusing the two modals cannot fully complement the depth information into the RGB modal. To enhance the quality of the depth image and integrate the cross-modal information effectively, we propose a depth enhanced cross-modal cascaded network (DCCNet) for RGB-D SOD. The entire cascaded network includes a depth cascaded branch, a RGB cascaded branch and a cross-modal fusion strategy. In the depth cascaded branch, we design a depth preprocessing algorithm to enhance the quality of the depth image. And in the process of depth feature extraction, we adopt four cascaded cross-modal guided modules to guide the RGB feature extraction process. In the RGB cascaded branch, we design five cascaded residual adaptive selection modules to output the RGB image feature extraction in each stage. In the cross-modal fusion strategy, a cross-modal channel-wise refinement is adopted to fuse the top-level features of the different modal feature branches. Finally, the multiscale loss is adopted to optimize the network training. Experimental results on six common RGB-D SOD datasets show that the performance of the proposed DCCNet is comparable to that of the state-of-the-art RGB-D SOD methods. [ABSTRACT FROM AUTHOR]

Nowadays the problem of image quality assessment (IQA) for screen content images (SCIs) has become a research hotspot as they are ubiquitous in multimedia applications. Although the quality assessment of natural images (NIs) has been continuously developed in the past few decades, few NI-oriented IQA methods can be directly applied on SCIs due to different visual characteristics between them. In this paper, we present a no-reference quality prediction approach considering the content information of SCIs, which is based on dual-channel multi-task convolutional neural network. First, we segment a SCI into small patches and classify them as the textual patches and the pictorial patches. Then, we devise a novel dual-channel convolutional neural network (CNN) to predict the quality of textual patches and pictorial patches. Finally, we propose an effective adaptive weighting strategy for quality score aggregation. The proposed CNN is built on an end-to-end multi-task learning framework, which assists the SCI quality prediction task through the histogram of oriented gradient (HOG) feature prediction task to learn a better mapping between the input patch and its quality score. The adaptive weighting strategy further improves the representation ability of each SCI patch. Experimental results on two largest SCI-oriented databases demonstrate that the proposed method outperforms most of the state-of-the-art no-reference IQA methods and the full-reference IQA methods. [ABSTRACT FROM AUTHOR]

Numerous screen content images (SCIs) have been produced to meet the needs of virtual desktop and remote display, which put forward a very urgent requirement for security and management of SCIs. Perceptual hashing is an effective way to deal with this issue. However, since SCIs are generally composed of pictures, graphics and texts, their intrinsic characteristics are different from those of natural images. Thus the previous hashing methods for natural images are not suitable for SCIs. In this article, we propose a perceptual hashing method for SCIs from the perspective of visual content understanding. Specifically, considering that the visual content understanding of SCIs mainly comes from textual regions, while the contours of text always have thinner width and higher contrast, it is decided to generate hash in the gradient field. An input screen image is first performed by some joint preprocessing operations. Then the maximum gradient magnitude and corresponding orientation information are extracted from three color channels R, G and B. Normalized histogram and local frequency coefficient features are further obtained from the maximum gradient magnitude. Finally, a hash sequence is constructed by statistics that are derived from extracted features. Experiments validated on three SCIs databases were conducted to evaluate classification between robustness and discrimination. Receiver operating characteristics (ROC) results demonstrate that the proposed method is superior to the state-of-the-art algorithms. Besides, SIQAD and SCID databases were leveraged to present the application in reduced-reference screen content image quality assessment, and comparisons show that our hashing could provide accurate predictions than other metrics. [ABSTRACT FROM AUTHOR]

Selective encryption algorithms have emerged as a popular technique for protecting the privacy of images during real-time transmission. For selectively encrypted images, it is necessary to evaluate their security and usability with visual security indices, and there have been a series of studies in this area. However, those proposed visual security indices (VSI) are often ineffective. We present a multi-directional structure and content-aware features-based visual security index (MCVSI) to perform an objective assessment on selectively encrypted images. Considering that selectively encrypted images prevent the main contents from being easily identified, stable local features in the images are extracted to indicate the degree of image content leakage. Meanwhile, we extract spatial structure information that closely aligns with human visual perception to indicate the level of variation in the overall image skeleton. Next, these features are subjected to similarity measurements to produce two types of similarity, content perception feature similarity and structure feature similarity. Finally, our visual security index is built by connecting all feature similarities and their corresponding visual security scores using the regression module. The experimental results and analyses indicate that the proposed MCVSI outperforms many existing mainstream VSI in terms of higher performance and stronger robustness, particularly on low and medium quality images. [ABSTRACT FROM AUTHOR]

With the development of technology, it is becoming easier and easier for people to generate tampered images that are indistinguishable from the human eye, and the malicious use of these photos in news, academic papers, and criminal crimes has brought great harm to society. This paper proposes a deep convolutional neural network-based image forgery localization method to uncover the subtle differences between doctored images and real images. Specifically, the network achieves tampering localization through a three-stage enhancement scheme. First, the dilated convolution in the deep layer of the network is used to keep the feature map resolution constant, and the number of shallow convolutions is decreased to reduce the perceptual field, so the network focuses on local regions. Second, the feature enhancement module is used to fuse shallow features with deep features to effectively filter content information and highlight tampering features, making full use of local and global information to improve the generalization ability. Finally, the attention enhancement module reweights the convolutional feature maps in terms of channels and locations, respectively, to highlight the information regions around the forgery boundaries, thus guiding the network to capture more intrinsic features for image forgery. Extensive experimental results on several public datasets show that this method outperforms other state-of-the-art methods in image forgery localization. [ABSTRACT FROM AUTHOR]