Duc V. Nguyen

Duc V. Nguyen, Nguyen Thi Quynh Ly, Truong Thu Huong

A dynamic 3D mesh is a key component in Virtual Reality applications. However, this type of content demands a significant processing resource for real-time rendering. To reduce processing requirements while preserving the user experience, adjusting the level of detail of 3D meshes based on viewing distance has been proposed. In this paper, we conduct an extensive subjective quality evaluation to investigate the effects of the level of detail and viewing distance on user perception of dynamic 3D meshes in a VR environment. Our evaluation results in a subjective dataset containing user ratings of 320 test stimuli generated from eight dynamic 3D meshes. Result analysis shows that it is possible to remove half of a mesh's faces without causing noticeable degradation in user Quality of Experience (QoE). An evaluation of popular objective quality metrics reveals that both 2D-based and 3D-based metrics have low correlation with subjective scores. Based on the subjective dataset, we develop a novel QoE prediction model that can accurately predict the MOS of a dynamic 3D mesh at a given level of detail and viewing distance. In addition, a QoE-aware resource allocation framework is proposed and evaluated under different resource constraints, showing significant improvement in the total QoE compared to conventional methods.

Vu Thi Hai Yen, Duc V. Nguyen, Cao Anh Minh Huy et al.

Short-form videos have become one of the most popular user-generated content formats nowadays. Popular short-video platforms use a simple streaming approach that preloads one or more videos in the recommendation list in advance. However, this approach results in significant data wastage, as a large portion of the downloaded video data is not used due to the user's early skip behavior. To address this problem, the chunk-based preloading approach has been proposed, where videos are divided into chunks, and preloading is performed in a chunk-based manner to reduce data wastage. To optimize chunk-based preloading, it is important to understand the user's viewing behavior in short-form video streaming. In this paper, we conduct a measurement study to construct a user behavior dataset that contains users' viewing times of one hundred short videos of various categories. Using the dataset, we evaluate the performance of standard time-series forecasting algorithms for predicting user viewing time in short-form video streaming. Our evaluation results show that Auto-ARIMA generally achieves the lowest and most stable forecasting errors across most experimental settings. The remaining methods, including AR, LR, SVR, and DTR, tend to produce higher errors and exhibit lower stability in many cases. The dataset is made publicly available at https://nvduc.github.io/shortvideodataset.

Duc V. Nguyen, Huyen T. T. Tran

Short video streaming platforms have gained immense popularity in recent years, transforming the way users consume video content. A critical aspect of user interaction with these platforms is the swipe gesture, which allows users to navigate through videos seamlessly. However, the delay between a user's swipe action and the subsequent video playback can significantly impact the overall user experience. This paper presents the first systematic study investigating the effects of swipe delay on user Quality of Experience (QoE) in short video streaming. In particular, we conduct a subjective quality assessment containing 132 swipe delay patterns. The obtained results show that user experience is affected not only by the swipe delay duration, but also by the number of delays and their temporal positions. A single delay of eight seconds or longer is likely to lead to user dissatisfaction. Moreover, early-session delays are less harmful to user QoE than late-session delays. Based on the findings, we propose a novel QoE model that accurately predicts user experience based on swipe delay characteristics. The proposed model demonstrates high correlation with subjective ratings, outperforming existing models in short video streaming.

Huyen T. T. Tran, Duc V. Nguyen, Nam Pham Ngoc et al.

Virtual Reality (VR), which brings immersive experiences to viewers, has been gaining popularity in recent years. A key feature in VR systems is the use of omnidirectional content, which provides 360-degree views of scenes. In this work, we study the human quality perception of omnidirectional images, focusing on different zones surrounding the foveation point. For that purpose, an extensive subjective experiment is carried out to assess the perceptual quality of omnidirectional images with non-uniform quality. Through experimental results, the impacts of different zones are analyzed. Moreover, nineteen objective quality metrics, including foveal quality metrics, are evaluated using our database. It is quantitatively shown that the zones corresponding to the fovea and parafovea of human eyes are extremely important for quality perception, while the impacts of the other zones corresponding to the perifovea and periphery are small. Besides, the investigated metrics are found to be not effective enough to reflect the quality perceived by viewers.