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Hashing method is an efficient technique of multimedia security for content protection. It maps an image into a content-based compact code for denoting the image itself. While most existing algorithms focus on improving the classification between robustness and discrimination, little attention has been paid to geometric invariance under normal digital operations, and therefore results in quite fragile to geometric distortion when applied in image copy detection. In this article, a novel effective image hashing method is proposed based on geometric invariant vector distance in both spatial domain and frequency domain. First, the image is preprocessed by some joint operations to extract robust features. Then, the preprocessed image is randomly divided into several overlapping blocks under a secret key, and two different feature matrices are separately obtained in the spatial domain and frequency domain through invariant moment and low frequency discrete cosine transform coefficients. Furthermore, the invariant distances between vectors in feature matrices are calculated and quantified to form a compact hash code. We conduct various experiments to demonstrate that the proposed hashing not only reaches good classification between robustness and discrimination, but also resists most geometric distortion in image copy detection. In addition, both receiver operating characteristics curve comparisons and mean average precision in copy detection clearly illustrate that the proposed hashing method outperforms state-of-the-art algorithms.

Connected and Automated Vehicle (CAV) is a transformative technology that has great potential to improve urban traffic and driving safety. Electric Vehicle (EV) is becoming the key subject of next-generation CAVs by virtue of its advantages in energy saving. Due to the limited endurance and computing capacity of EVs, it is challenging to meet the surging demand for computing-intensive and delay-sensitive in-vehicle intelligent applications. Therefore, computation offloading has been employed to extend a single vehicle’s computing capacity. Although various offloading strategies have been proposed to achieve good computing performace in the Vehicular Edge Computing (VEC) environment, it remains challenging to jointly optimize the offloading failure rate and the total energy consumption of the offloading process. To address this challenge, in this paper, we establish a computation offloading model based on Markov Decision Process (MDP), taking into consideration task dependencies, vehicle mobility, and different computing resources for task offloading. We then design a computation offloading strategy based on deep reinforcement learning, and leverage the Deep Q-Network based on Simulated Annealing (SA-DQN) algorithm to optimize the joint objectives. Experimental results show that the proposed strategy effectively reduces the offloading failure rate and the total energy consumption for application offloading.

The past decades have witnessed growing development of image quality assessment (IQA) for natural images (NIs). However, since screen content images (SCIs) exhibit different visual characteristics from the NIs, few of NIs-oriented IQA methods can be directly applied on SCIs. In this paper, we present a quality prediction approach specially designed for SCIs, which is based on multi-task deep learning. First, we split a SCI into \(32\times 32\) patches and design a novel convolutional neural network (CNN) to predict the quality score of each SCI patch. Then, we propose an effective adaptive weighting algorithm for patch-level quality score aggregation. The proposed CNN is built on an end-to-end multi-task learning framework, which integrates the histogram of oriented gradient (HOG) features prediction task to the SCI quality prediction task for learning a better mapping between input SCI patch and its quality score. The proposed adaptive weighting algorithm for patch-level quality score aggregation further improves the representation ability of each SCI patch. Experimental results on two-largest SCI-oriented databases demonstrate that our proposed method is superior to the state-of-the-art no-reference IQA methods and most of the full-reference IQA methods.

We investigate the use of MDS in image hashing.We propose an MDS-based hashing resistant to any-angle rotation.Rotation-invariant feature matrix is constructed by LPT and DFT.MDS is used to learn a compact and discriminative representation.Our hashing outperforms some state-of-the-art algorithms in classification performance. Multidimensional scaling (MDS) is an efficient technique for data analysis, and has been successfully applied in data visualization, object retrieval, data clustering, and so on. However, its use in image hashing is rarely discussed yet. In this study, we investigate the use of MDS in image hashing and propose an MDS-based hashing algorithm resistant to any-angle rotation. The proposed algorithm extracts a rotation-invariant feature matrix with log-polar transform and discrete Fourier transform from the normalized image, and learns a compact and discriminative representation from the feature matrix by MDS. Experiments with 3845 images are carried out and the results demonstrate that the proposed algorithm is robust to many content-preserving operations, including any-angle rotation, and reaches good discrimination. Receiver operating characteristics (ROC) curve comparisons illustrate that the proposed algorithm outperforms some state-of-the-art algorithms in classification with respect to robustness and discrimination.

Nanoplastics derived from degradation of micro- or macroplastics are emerging contaminants in aquatic environments, where their fate and transport as well as toxicity are affected by aggregation. This study employed time-resolved dynamic light scattering to investigate the aggregation kinetics of polystyrene nanoplastics (PSNPs) in the presence of four macromolecules (sodium alginate (SA), bovine serum albumin (BSA), extracellular polymeric substance (EPS), and Suwannee River humic acid (HA)) in solutions containing monovalent (NaCl) and divalent (CaCl2) salts at different pH. Our results showed that the macromolecules enhanced PSNP stability in NaCl solutions but destabilized PSNPs in CaCl2 solutions at pH 6. In NaCl solutions, macromolecules inhibited PSNP aggregation due to steric hindrance originated from macromolecular layer adsorbed on PSNPs. The strongest stabilization effect was observed for BSA having the greatest hydrodynamic adsorption layer thickness of 21.9 nm, followed by HA, EPS, and SA. In CaCl2 solutions, SA significantly destabilized PSNPs via alginate bridging with Ca2+, which enhanced with concentrations of SA and CaCl2. The destabilization effects of other three macromolecules in CaCl2 solutions were governed by the interplay among molecular bridging, charge screening, and steric hindrance. An increased pH in NaCl or CaCl2 solutions containing macromolecules all stabilized PSNPs due to elevated electrostatic repulsion, except that SA destabilized PSNPs in CaCl2 solutions via enhanced molecular bridging. The stabilization effect of macromolecules may also compete with the destabilization effect under seawater condition. This study suggested that PSNP aggregation in aquatic environments could be strongly affected by macromolecules and solution chemistry.

Image hashing is a novel technology of multimedia processing with wide applications. Robustness and discrimination are two of the most important objectives of image hashing. Different from existing hashing methods without a good balance with respect to robustness and discrimination, which largely restrict the application in image retrieval and copy detection, i.e., seriously reducing the retrieval accuracy of similar images, we propose a new hashing method which can preserve two kinds of complementary features (global feature via texture and local feature via DCT coefficients) to achieve a good balance between robustness and discrimination. Specifically, the statistical characteristics in gray-level co-occurrence matrix (GLCM) are extracted to well reveal the texture changes of an image, which is of great benefit to improve the perceptual robustness. Then, the normalized image is divided into image blocks, and the dominant DCT coefficients in the first row/column are selected to form a feature matrix. The Euclidean distance between vectors of the feature matrix is invariant to commonly-used digital operations, which helps make hash more compact. Various experiments show that our approach achieves a better balance between robustness and discrimination than the state-of-the-art algorithms.