Classification of electroencephalogram (EEG) signals holds significant implications for assisting clinical diagnosis, treatment, and monitoring. Nonetheless, this domain encounters several challenges arising from the diversity, complexity, and paucity of EEG data. Therefore, this article proposes transfer adaptive dictionary learning with intraclass low-rank regularization (TADL-ICLR) for EEG signal classification. Within the framework of multisource domain transfer dictionary learning, TADL-ICLR employs projection matrices to project multiple source domains and target domain into a common subspace. This algorithm seeks a common dictionary across different domains to extract underlying data information, allowing data from various domains to be represented by similar sparse coding. Based on multidomain projected data and their sparse coding, TADL-ICLR first establishes an adaptive local linear embedding term to uncover the intrinsic geometric structure of data across domains. Second, TADL-ICLR introduces intraclass low-rank regularization, which imposes a low-rank structure on sparse coding with class information to counteract the blindness of the common dictionary and uncover latent class-discriminative information in the subspace. Third, TADL-ICLR incorporates an adaptive classifier with active samples, utilizing not only labeled samples but also the unlabeled samples in the target domain to enhance the dictionary’s discriminative power. Experimental results on public datasets demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms.

More and more users are getting used to posting images and text on social networks to share their emotions or opinions. Accordingly, multimodal sentiment analysis has become a research topic of increasing interest in recent years. Typically, there exist affective regions that evoke human sentiment in an image, which are usually manifested by corresponding words in people's comments. Similarly, people also tend to portray the affective regions of an image when composing image descriptions. As a result, the relationship between image affective regions and the associated text is of great significance for multimodal sentiment analysis. However, most of the existing multimodal sentiment analysis approaches simply concatenate features from image and text, which could not fully explore the interaction between them, leading to suboptimal results. Motivated by this observation, we propose a new image-text interaction network (ITIN) to investigate the relationship between affective image regions and text for multimodal sentiment analysis. Specifically, we introduce a cross-modal alignment module to capture region-word correspondence, based on which multimodal features are fused through an adaptive cross-modal gating module. Moreover, considering the complementary role of context information on sentiment analysis, we integrate the individual-modal contextual feature representations for achieving more reliable prediction. Extensive experimental results and comparisons on public datasets demonstrate that the proposed model is superior to the state-of-the-art methods.

Abstract Rapid economic growth has caused severe environmental pollution, which has aroused great concern. This pollution affects public health and impairs visibility, therefore, it should be given greater consideration. In this paper, a photo‐based PM2.5 concentration predictor is proposed based on the natural scene statistics without artificial assistance or extra information. Given that the quality of PM2.5 concentration images is determined by many factors, three types of influencing factors are analysed: the colourfulness, the structural degradation and the contrast. The first feature consists of the hue, saturation and colour descriptors, which measure the colourfulness of the PM2.5 concentration images. The second feature is determined based on the contrast can effectively portray the quality of PM2.5 concentration in the images. The third feature is extracted based on the natural scene statistics model, which measures the local and global structural degradation information and the naturalness of the PM2.5 concentration images. Finally, the three features are used to train a random forest model that can be used to predict the concentration of PM2.5. Experimental results illustrate that the performance of the proposed model is better than those of popular competitors on AQID.

Blind image quality assessment (BIQA) for authentic distortions is still a great challenge, even in today’s deep learning era. It has been widely acknowledged that local and global features are both indispensable for IQA, which play complementary roles. While combining local and global features is straightforward in traditional handcrafted feature-based IQA metrics, it is not an easy task in the deep learning framework. This is mainly due to the fact that deep neural networks typically require input images with a fixed size. Current metrics either resize the image or use local patches as input, which are problematic in that they cannot integrate local and global aspects as well as their interactions to achieve comprehensive quality evaluation. Motivated by the above facts, this paper presents a new BIQA metric for authentic distortions by aggregating local and global deep features in a Vision-Transformer framework. In the proposed metric, selective local regions and global content are simultaneously input for complementary feature extraction, and the Vision-Transformer is employed to build the relationship between different local patches and image quality. Self-attention mechanism is further adopted to explore the interaction between local and global deep features, producing the final image quality score. Extensive experiments on five authentically distorted IQA databases demonstrate that the proposed metric outperforms the state-of-the-arts in terms of both prediction performance and generalization ability.

With the boom of deep neural networks, blind image quality assessment (BIQA) has achieved great processes. However, the current BIQA metrics are limited when evaluating low-quality images as compared to medium-quality and high-quality images, which restricts their applications in real world problems. In this paper, we first identify that two challenges caused by distribution shift and long-tailed distribution lead to the compromised performance on low-quality images. Then, we propose an intermediary enhancement-based bilateral network with iterative training strategy for solving these two challenges. Drawing on the experience of transitive transfer learning, the proposed metric adaptively introduces enhanced intermediary images to transfer more information to low-quality images for mitigating the distribution shift. Our metric also adopts an iterative training strategy to deal with the long-tailed distribution. This strategy decouples feature extraction and score regression for better representation learning and regressor training. It not only transfers the knowledge learned from the earlier stage to the latter stage, but also makes the model pay more attention to long-tailed low-quality images. We conduct extensive experiments on five authentically distorted image quality datasets. The results show that our metric significantly improves the evaluating performance on low-quality images and delivers state-of-the-art intra-dataset results. During generalization tests, our metric also achieves the best cross-dataset performance.

Image restoration has attracted substantial attention recently and many image restoration algorithms have been proposed for restoring latent clear images from degraded images. However, determining how to objectively evaluate the performances of these algorithms remains an open problem, which may hinder the further development of advanced image restoration techniques. Most image restoration-quality metrics are designed for specific restoration applications; hence, their generalization ability is limited. For benchmarking image restoration algorithms, the ranking of restored images that are generated via various algorithms, is the most heavily considered factor. Inspired by this, this paper presents a pairwise-comparison-based rank learning framework for benchmarking the performances of image restoration algorithms, which focuses on the relative quality ranking of restored images. Under the proposed framework, we further propose a general image restoration quality metric by integrating quality-aware features in both the spatial and frequency domains. The proposed metric exhibits good generalization performance, and it is applicable to various restoration applications. The results of extensive experiments that were conducted on eight public databases of five restoration scenarios demonstrate the superior performance of the proposed method over the existing quality metrics. Moreover, the proposed framework is used to improve the existing quality metrics for benchmarking image restoration algorithms and highly encouraging results are obtained.

Image deblocking has been widely studied. However, the relevant quality evaluation of deblocked images remains an open problem. The deblocked images are usually contaminated by multiple distortions, typically blocking artifacts and blur. Although various quality metrics have been reported, they are not designed specially for deblocked images, so they cannot accurately predict the quality of deblocked images. To fill this gap, we propose a new quality metric for deblocked images. With the guidance of the internal generative mechanism (IG- M) theory, a deblocked image is first decomposed into two portions, i.e., the predicted and disorderly portions. Then the distortions in the predicted portion are evaluated. Specifically, the distortion-specific features are extracted to evaluate blocking artifacts and blur in the spatial domain, separately. The joint effect of blocking artifacts and blur is evaluated by extracting energy-based features in the Curvelet domain. Finally, all features are combined to train a random forest model for quality prediction of deblocked images. Experimental results conducted on a newly released DeBlocked Image Database (DBID) demonstrate that the proposed metric outperforms the existing relevant quality metrics.

Machine-to-machine (M2M) type communications (MTCs) over cellular networks feature the large number of MTC devices (MTCDs), small and time controlled data transmissions, and rigorous energy limitation. Considering full-duplex (FD) relaying can achieve high spectrum and energy efficiency, this paper proposes an MTC-enabled cellular communication scheme, where a traditional cellular user equipment (UE) is configured as an FD relaying based gateway to assist the uplink transmissions of the served MTCDs. The designed objective is to minimize the aggregate energy consumption of a group consisting of a UE and multiple MTCDs, while fulfilling their minimum throughput requirements. To this end, a convex optimization problem is formulated and a low complexity algorithm is also developed to find the optimal power allocation strategies for the UE and the MTCDs. The simulation results show that the proposed scheme can achieve most of the channel reuse gain of the FD relaying if the self-interference at the UE is controlled below a certain level.

Motion deblurring has been widely studied. However, the relevant quality evaluation of motion deblurred images remains an open problem. The motion deblurred images are usually contaminated by noise, ringing and residual blur (NRRB) simultaneously. Unfortunately, most of the existing quality metrics are not designed for multiply distorted images, so they are limited in predicting the quality of motion deblurred images. In this study, the authors propose a new quality metric for motion deblurred images by measuring NRRB. For a motion deblurred image, the noise level is first estimated. Then the ringing effect is measured by incorporating visual saliency model to adapt to the characteristic of the human visual system. A reblurring‐based method is proposed to extract similarity features between a motion deblurred image and its re‐blurred version for evaluating the residual blur. Finally, the overall quality score of a motion deblurred image is obtained by pooling the scores of noise, ringing and blur. Experimental results conducted on a motion deblurring database demonstrate that the proposed metric significantly outperforms the existing quality metrics. In addition, the proposed NRRB metric is used for improving the existing general‐purpose no‐reference metrics, and very encouraging results are achieved.

In recent years, single-image super-resolution (SR) reconstruction has aroused wide attention. Massive SR enhancement algorithms have been proposed. However, much less work has been down on the perceptual evaluation of SR enhanced images and the corresponding enhancement algorithms. In this work, we create a Super-resolution Reconstructed Image Database (SRID), which consists of images produced by two interpolation methods and six popular SR image enhancement algorithms at different amplification factors. Then, subjective experiment is conducted to collect the subjective scores by using the single-stimulus method. The performances of the SR image enhancement algorithms are then evaluated by the obtained subjective scores. Finally, the performances of the general-purpose no-reference (NR) image quality metrics are investigated on the SRID database. This study shows that it is difficult for the state-of-the-art NR image quality metrics to predict the quality of SR enhanced images.

Compressive sensing (CS) has been attracting tremendous attention in recent years. Extensive CS recovery algorithms have been proposed for effective image reconstruction. However, little work has been dedicated to the perceptual evaluation of CS image recovery algorithms and the corresponding recovered images. In this paper, we first build a Compressive Sensing Recovered Image Database (CSRID), which contains images generated by ten popular CS image recovery algorithms at different sensing rates. We then carry out a subjective experiment using the single-stimulus method to obtain the subjective qualities of the images. The subjective scores are then used to evaluate the performances of the CS image recovery algorithms. Finally, the performances of general-purpose no-reference (NR) quality metrics and image blur metrics are investigated on the CSRID database. Experimental results show that the state-of-the-art quality metrics are very limited in predicting the quality of CS recovered images.

This letter presents a Referenceless quality Measure of Blocking artifacts (RMB) using Tchebichef moments. It is based on the observation that Tchebichef kernels with different orders have varying abilities to capture blockiness. In a block manner, high-odd-order moments are computed to score the blocking artifacts. The blockiness scores are further weighted to incorporate the characteristic of Human Visual System (HVS), which is achieved by classifying the blocks into smooth and textured. Experimental results and comparisons demonstrate the advantage of the proposed method.

A novel robust image watermarking scheme is presented to embed a high capacity watermark into the feature point based characteristic regions. The watermark embedding positions are first determined by the scale-invariant feature transform (SIFT) based local circular regions. Then the binary watermark image is embedded by quantization in the Non-subsampled Contourlet Transform (NSCT) domain. In order to achieve rotation invariance, the watermark is embedded adaptively to the orientation of the region. Simulation results show that the proposed scheme can achieve high invisibility and it can efficiently resist traditional signal processing attacks and geometric attacks.

The novel coal-gas dangerous-level prediction model established has advantages of the EA and BP neural nets, and overcomes the shortcomings of misreport and missing-report of others. This approach can accurately capture the complicated relationships among feature values of coal-gas outbursts and dangerous circumstances. We considered the characteristic of coal-gas outburst carefully, combining with the raw data of coal-gas monitor system in the Daping colliery and the 10thcolliery of Pingdingshan Company as well as real-time samples of accidents, and selected pattern sets to train the proposed model and generate the corresponding rules for prediction. Results show that the ENN has better performance than the ANN or the traditional method used individually, and enhances the practical techniques for prediction of coal and gas in coal mine to guarantee safety.