We present HoloQA, a new state-of-the-art Full Reference Video Quality Assessment (VQA) model that was designed using principles of visual neuroscience, information theory, and self-supervised deep learning to accurately predict the quality of rendered digital human avatars in Virtual Reality (VR) and Augmented Reality (AR) systems. The growing adoption of VR/AR applications that aim to transmit digital human avatars over bandwidth-limited video networks has driven the need for VQA algorithms that better account for the kinds of distortions that reduce the quality of rendered and viewed avatars. As we will show, standard VQA models often fail to capture distortions unique to the rendering, transmission, and compression of videos containing human avatars. Towards solving this difficult problem, we adopt a multi-level Mixture-of-Experts approach. This involves computing distortion-aware perceptual features and high-level content-aware deep features that capture semantic attributes of human body avatars. The high-level features are computed using a self-supervised, pre-trained deep learning network. We show that HoloQA is able to achieve state-of-the-art performance on the recently introduced LIVE-Meta Rendered Human Avatar VQA database, demonstrating its efficacy in predicting the quality of rendered human avatars in VR. Furthermore, we demonstrate the competitive performance of HoloQA on other digital human avatar databases and on another synthetically generated video quality use case: cloud gaming. The code associated with this work will be made available on https://github.com/avinabsaha/HologramQAGitHub.

We study the visual quality judgments of human subjects on digital human avatars (sometimes referred to as "holograms" in the parlance of virtual reality [VR] and augmented reality [AR] systems) that have been subjected to distortions. We also study the ability of video quality models to predict human judgments. As streaming human avatar videos in VR or AR become increasingly common, the need for more advanced human avatar video compression protocols will be required to address the tradeoffs between faithfully transmitting high-quality visual representations while adjusting to changeable bandwidth scenarios. During transmission over the internet, the perceived quality of compressed human avatar videos can be severely impaired by visual artifacts. To optimize trade-offs between perceptual quality and data volume in practical workflows, video quality assessment (VQA) models are essential tools. However, there are very few VQA algorithms developed specifically to analyze human body avatar videos, due, at least in part, to the dearth of appropriate and comprehensive datasets of adequate size. Towards filling this gap, we introduce the LIVE-Meta Rendered Human Avatar VQA Database, which contains 720 human avatar videos processed using 20 different combinations of encoding parameters, labeled by corresponding human perceptual quality judgments that were collected in six degrees of freedom VR headsets. To demonstrate the usefulness of this new and unique video resource, we use it to study and compare the performances of a variety of state-of-the-art Full Reference and No Reference video quality prediction models, including a new model called HoloQA. As a service to the research community, we publicly releases the metadata of the new database at https://live.ece.utexas.edu/research/LIVE-Meta-rendered-human-avatar/index.html.

The mobile cloud gaming industry has been rapidly growing over the last decade. When streaming gaming videos are transmitted to customers' client devices from cloud servers, algorithms that can monitor distorted video quality without having any reference video available are desirable tools. However, creating No-Reference Video Quality Assessment (NR VQA) models that can accurately predict the quality of streaming gaming videos rendered by computer graphics engines is a challenging problem, since gaming content generally differs statistically from naturalistic videos, often lacks detail, and contains many smooth regions. Until recently, the problem has been further complicated by the lack of adequate subjective quality databases of mobile gaming content. We have created a new gaming-specific NR VQA model called the Gaming Video Quality Evaluator (GAMIVAL), which combines and leverages the advantages of spatial and temporal gaming distorted scene statistics models, a neural noise model, and deep semantic features. Using a support vector regression (SVR) as a regressor, GAMIVAL achieves superior performance on the new LIVE-Meta Mobile Cloud Gaming (LIVE-Meta MCG) video quality database.

We present the outcomes of a recent large-scale subjective study of Mobile Cloud Gaming Video Quality Assessment (MCG-VQA) on a diverse set of gaming videos. Rapid advancements in cloud services, faster video encoding technologies, and increased access to high-speed, low-latency wireless internet have all contributed to the exponential growth of the Mobile Cloud Gaming industry. Consequently, the development of methods to assess the quality of real-time video feeds to end-users of cloud gaming platforms has become increasingly important. However, due to the lack of a large-scale public Mobile Cloud Gaming Video dataset containing a diverse set of distorted videos with corresponding subjective scores, there has been limited work on the development of MCG-VQA models. Towards accelerating progress towards these goals, we created a new dataset, named the LIVE-Meta Mobile Cloud Gaming (LIVE-Meta-MCG) video quality database, composed of 600 landscape and portrait gaming videos, on which we collected 14,400 subjective quality ratings from an in-lab subjective study. Additionally, to demonstrate the usefulness of the new resource, we benchmarked multiple state-of-the-art VQA algorithms on the database. The new database will be made publicly available on our website: https://live.ece.utexas.edu/research/LIVE-Meta-Mobile-Cloud-Gaming/index.html.

C is a popular programming language because of high portability and simple syntax. As a result, it is the first course for most of university students who major in computer, electronics, and electrical engineering and so on. To obtain a good learning achievement, students usually need to put their effort and time on programming as much as possible. Therefore, a ubiquitous integrated development environment is helpful for students to practice programming anytime and anywhere. To achieve this goal, we develop a novel integrated development environment called Ubi-C for C programming based on Clang and LLVM in this paper. Using this IDE, users can directly write, compile, execute and debug their C programs on Android-based smart phones or tablets. Moreover, this IDE provides the API of multimedia programming, and integrates with cloud services such as Dropbox for storing user programs and data. Consequently, users can easily develop multimedia applications by using C on mobile devices, and can switch their working environments between mobile devices and fixed computers with the support of Dropbox.

The concept of random walk (RW) has been widely applied in the design of recommendation systems. RW-based approaches are effective in handling locality problem and taking extra information, such as the relationships between items or users, into consideration. However, the traditional RW-based approach has a serious limitation in handling bidirectional opinions. The propagation of positive and negative information simultaneously in a graph is nontrivial using random walk. To address the problem, this article presents a novel and efficient RW-based model that can handle both positive and negative comments with the guarantee of convergence. Furthermore, we argue that a good recommendation system should provide users not only a list of recommended items but also reasonable explanations for the decisions. Therefore, we propose a technique that generates explanations by backtracking the influential paths and subgraphs. The results of experiments on the MovieLens and Netflix datasets show that our model significantly outperforms state-of-the-art RW-based algorithms, and is capable of improving the overall performance in the ensemble with other models.