by Minh Nguyen, Shivi Vats, Sam Van Damme, Jeroen Van Der Hooft, Maria Torres Vega, Tim Wauters, Filip de Turck, Christian Timmerer, and Hermann Hellwagner.
Point cloud streaming has recently attracted research attention as it has the potential to provide six degrees of freedom movement, which is essential for truly immersive media. The transmission of point clouds requires high-bandwidth connections, and adaptive streaming is a promising solution to cope with fluctuating bandwidth conditions. Thus, understanding the impact of different factors in adaptive streaming on the Quality of Experience (QoE) becomes fundamental. Point clouds have been evaluated in Virtual Reality (VR), where viewers are completely immersed in a virtual environment. Augmented Reality (AR) is a novel technology and has recently become popular, yet quality evaluations of point clouds in AR environments are still limited to static images. In this paper, we perform a subjective study of four impact factors on the QoE of point cloud video sequences in AR conditions, including encoding parameters (quantization parameters, QPs), quality switches, viewing distance, and content characteristics.
The experimental results show that these factors significantly impact the QoE. The QoE decreases if the sequence is encoded at high QPs and/or switches to lower quality and/or is viewed at a shorter distance, and vice versa. Additionally, the results indicate that the end user is not able to distinguish the quality differences between two quality levels at a specific (high) viewing distance. An intermediate-quality point cloud encoded at geometry QP (G-QP) 24 and texture QP (T-QP) 32 and viewed at 2.5m can have a QoE (i.e., score 6.5 out of 10) comparable to a high-quality point cloud encoded at 16 and 22 for G-QP and T-QP, respectively, and viewed at a distance of 5 m. Regarding content characteristics, objects with lower contrast can yield better quality scores. Participants’ responses reveal that the visual quality of point clouds has not yet reached an immersion level as desired. The average QoE of the highest visual quality is less than 8 out of 10. There is also a good correlation between objective metrics (e.g., color Peak Signal-to-Noise Ratio (PSNR) and geometry PSNR) and the QoE score. Especially the Pearson correlation coefficients of color PSNR is 0.84. Finally, we found that machine learning models are able to accurately predict the QoE of point clouds in AR environments.
by Sam Damme, Imen Mahdi, Hemanth Kumar Ravuri, Jeroen van der Hooft, Filip De Turck, and Maria Torres Vega. Proceedings of 15th International Conference on Quality of Multimedia Experience (QoMEX 2023)
Dynamic point cloud delivery can provide the required interactivity and realism to six degrees of freedom (6DoF) interactive applications. However, dynamic point cloud rendering imposes stringent requirements (e.g., frames per second (FPS) and quality) that current hardware cannot handle. A possible solution is to convert point cloud into meshes before rendering on the head-mounted display (HMD). However, this conversion can induce degradation in quality perception such as a change in depth, level of detail, or presence of artifacts. This paper, as one of the first, presents an extensive subjective study of the effects of converting point cloud to meshes with different quality representations.
In addition, we provide a novel in-session content rating methodology, providing a more accurate assessment as well as avoiding post-study bias. Our study shows that both compression level and observation distance have their influence on subjective perception. However, the degree of influence is heavily entangled with the content and geometry at hand. Furthermore, we also noticed that while end users are clearly aware of quality switches, the influence on their quality perception is limited. As a result, this has the potential to open up possibilities in bringing the adaptive video streaming paradigm to the 6DoF environment.
by Minh Nguyen, Shivi Vats, Sam Van Damme, Jeroen van der Hooft, Maria Torres Vega, Tim WautersChristian Timmerer, and Hermann Hellwagner. Proceedings of 15th International Conference on Quality of Multimedia Experience (QoMEX 2023).
Point Cloud (PC) streaming has recently attracted research attention as it has the potential to provide six degrees of freedom (6DoF), which is essential for truly immersive media.
PCs require high-bandwidth connections, and adaptive streaming is a promising solution to cope with fluctuating bandwidth conditions. Thus, understanding the impact of different factors in adaptive streaming on the Quality of Experience (QoE) becomes fundamental. Mixed Reality (MR) is a novel technology and has recently become popular. However, quality evaluations of PCs in MR environments are still limited to static images. In this paper, we perform a subjective study on four impact factors on the QoE of PC video sequences in MR conditions, including quality switches, viewing distance, and content characteristics.
The experimental results show that these factors significantly impact QoE. The QoE decreases if the sequence switches to lower quality and/or is viewed at a shorter distance, and vice versa. Additionally, the end user might not distinguish the quality differences between two quality levels at a specific viewing distance. Regarding content characteristics, objects with lower contrast seem to provide better quality scores.
by Shivi Vats, Minh Nguyen, Sam Van Damme, Jeroen van der Hooft, Maria Torres Vega, Tim Wauters, Christian Timmerer, Hermann Hellwagner. Proceedings of 15th International Conference on Quality of Multimedia Experience (QoMEX 2023).
3D objects are important components in Mixed Reality (MR) environments as they allow users to inspect and interact with them in a six degrees of freedom (6DoF) system.
Point clouds (PCs) and meshes are two common 3D object representations that can be compressed to reduce the delivered data at the cost of quality degradation. In addition, as the end users can move around in 6DoF applications, the viewing distance can vary. Quality assessment is necessary to evaluate the impact of the compressed representation and viewing distance on the Quality of Experience (QoE) of end users. This paper presents a demonstrator for subjective quality assessment of dynamic PC and mesh objects under different conditions in MR environments.
Our platform allows conducting subjective tests to evaluate various QoE influence factors, including encoding parameters, quality switching, viewing distance, and content characteristics, with configurable settings for these factors.