Omnidirectional image (OI) quality assessment is crucial to facilitate the development of virtual reality (VR) related technology. In this work, a distortion discrimination assisted multi-stream network is proposed for OI quality assessment. The multi-stream architecture is constructed by generating the viewport images received by the retina at one point to simulate the characteristics of humans perceiving VR contents. Additionally, the strategy of generating several viewport image sets from one OI is proposed for data augmentation. Furthermore, the facts that the human brain has the ability for both quality assessment and distortion type distinguishment, and the process of human brain handling two tasks exists information interaction inspire us to employ an auxiliary distortion discrimination task to facilitate the quality assessment task learning. Extensive experiments conducted on two public OI databases demonstrate the superiority of the proposed method to both traditional 2D quality metrics and existing metrics specific for OIs. Moreover, utilizing the assistant task is proven to be more effective than the single task learning for OI quality evaluation. Better generalization performance is also verified to be another valuable trait of the proposed method.

View synthesis is a key technique in free-viewpoint video, which renders virtual views based on texture and depth images. The distortions in synthesized views come from two stages, i.e., the stage of the acquisition and processing of texture and depth images, and the rendering stage using depth-image-based-rendering (DIBR) algorithms. The existing view synthesis quality metrics are designed for the distortions caused by a single stage, which cannot accurately evaluate the quality of the entire view synthesis process. With the considerations that the distortions introduced by two stages both cause edge degradation and texture unnaturalness, and the Difference-of-Gaussian (DoG) representation is powerful in capturing image edge and texture characteristics by simulating the center-surrounding receptive fields of retinal ganglion cells of human eyes, this paper presents a no-reference quality index for Synthesized views using DoG-based Edge statistics and Texture naturalness (SET). To mimic the multi-scale property of the Human Visual System (HVS), DoG images are first calculated at multiple scales. Then the orientation selective statistics features and the texture naturalness features are calculated on the DoG images and the coarsest scale image, producing two groups of quality-aware features. Finally, the quality model is learnt from these features using the random forest regression model. Experimental results on two view synthesis image databases demonstrate that the proposed metric is advantageous over the relevant state-of-the-arts in dealing with the distortions in the whole view synthesis process.

In the past decade, extensive image quality metrics have been proposed. The majority of them are tailored for the images that contain a specific type of distortion. However, in practice, the images are usually degraded by different types of distortions simultaneously. This poses great challenges to the existing quality metrics. Motivated by this, this paper proposes a no-reference quality index for the multiply distorted images using the biorder structure degradation and the nonlocal statistics. The design philosophy is inspired by the fact that the human visual system (HVS) is highly sensitive to the degradations of both the spatial contrast and the spatial distribution, which are prone to be changed by the joint effects of the multiple distortions. Specifically, the multiresolution representation of the image is first built by downsampling to simulate the hierarchical property of the HVS. Then, the structure degradation is calculated to measure the spatial contrast. Considering the fact that the human visual cortex has the separate mechanisms to perceive the first- and second-order structures, dubbed biorder structures, the degradations of biorder structures are calculated to account for the spatial contrast, producing the first group of the quality-aware features. Furthermore, the nonlocal self-similarity statistics is calculated to measure the spatial distribution, producing the second group of features. Finally, all the features are fed into the random forest regression model to learn the quality model for the multiply distorted images. Extensive experimental results conducted on the three public databases demonstrate the superiority of the proposed metric to the state-of-the-art metrics. Moreover, the proposed metric is also advantageous over the existing metrics in terms of the generalization ability.

Virtual view synthesis plays a vital role in the application of multi-view and free-viewpoint videos. Depth-image-based rendering (DIBR) is the most commonly used approach in view synthesis, and many DIBR algorithms have been proposed. However, how to evaluate the quality of DIBR-synthesized images and benchmark the DIBR algorithms are still very challenging, which may hinder the further development of the view synthesis technique. Hence, an effective quality metric for evaluating the distortions in view synthesis is urgently needed. With this motivation, this paper presents a quality index for view synthesis by simultaneously measuring local Instance DEgradation and global Appearance (IDEA). Due to the imperfection of rendering algorithms, local geometric distortions are easily introduced around instance contours, causing instance degradation, which is the dominant distortion in synthesized views. In this work, image instances are first detected and local instance degradation is measured based on discrete orthogonal moments. Meantime, we propose to measure the global appearance of synthesized images based on the superpixel representation. By integrating both local and global aspects of the distortions, a more accurate quality model is built for view synthesis. Extensive experiments and comparisons have demonstrated the superiority of the proposed method in evaluating the quality of DIBR-synthesized images and benchmarking the performance of view synthesis algorithms.

A variety of emergency events such as traffic accidents often happen in urban areas and the related emergency information needs to be urgently diffused to vehicles that may pass through the accident place to avoid escalation of the event. However, due to the dynamics of vehicles, the information diffusion links between vehicles and roadside units (RSUs) may be unstable, and thus the information diffusion based on fixed RSUs may lead to the low information propagation rate and the limited influence range. To address the above issues, we propose an emergency information diffusion strategy based on the Social Internet of Vehicle (SIoV), where vehicles can build inter-vehicle social relationships without human intervention and exchange emergency information through stable vehicle-to-vehicle (V2V) links. In detail, we design a $b$ -matching based stable link construction algorithm to build stable V2V links by considering the social characteristics and connection probability simultaneously, and then depict the vehicle link graph. Based on this graph, we formulate the information diffusion problem as an influence maximization problem. To solve this problem, we design an SIoV based emergency information influence maximization (SEIM) algorithm to maximize the influence range by selecting some influential seed vehicles and boosting some vehicles’ influence. The theoretical analysis and simulation results show that our algorithms have a lower dissemination delay and larger influence range by sacrificing part of the signaling overheads performance.

Panoramic image quality assessment (PIQA) is crucial to the successful application of technologies that can provide immersive visual experience. Stitching distortions are one of the main types of distortions that result in panoramic image degradation. However, most existing PIQA methods are general-purpose ones, which ignore the special characteristics of the stitching distortions caused by imperfect stitching algorithms. This results in unsatisfactory performance. To this end, we propose an effective stitched PIQA method, which consists of an imaginary reference generation (IRG) module and a hierarchical quality prediction (HQP) module. Among them, the IRG module is proposed to mimic the capability of the human visual system in imagining the raw version in the face of a degraded image. For the IRG module learning, we construct a large-scale database. The HQP module is presented to adapt to the particularity and complexity of stitching distortions, which is achieved by the pyramid feature aggregation. Extensive experiments and comparisons have been performed on the stitched PIQA database and the experimental results demonstrate the superiority of the proposed method in evaluating the quality of stitched panoramic images.