Point clouds have become increasingly prevalent in representing 3D scenes within virtual environments, alongside 3D meshes. Their ease of capture has facilitated a wide array of applications on mobile devices, from smartphones to autonomous vehicles. Notably, point cloud compression has reached an advanced stage and has been standardized. However, the availability of quality assessment datasets, which are essential for developing improved objective quality metrics, remains limited. In this paper, we introduce BASICS, a large-scale quality assessment dataset tailored for static point clouds. The BASICS dataset comprises 75 unique point clouds, each compressed with four different algorithms including a learning-based method, resulting in the evaluation of nearly 1500 point clouds by 3500 unique participants. Furthermore, we conduct a comprehensive analysis of the gathered data, benchmark existing point cloud quality assessment metrics and identify their limitations. By publicly releasing the BASICS dataset, we lay the foundation for addressing these limitations and fostering the development of more precise quality metrics.
University of Science and Technology of China（中国科学技术大学）
Image compression has raised widespread interest recently due to its significant importance for multimedia storage and transmission. Meanwhile, a reliable image quality assessment (IQA) for compressed images can not only help to verify the performance of various compression algorithms but also help to guide the compression optimization in turn. In this paper, we design a full-reference image quality assessment metric SwinIQA to measure the perceptual quality of compressed images in a learned Swin distance space. It is known that the compression artifacts are usually non-uniformly distributed w…
With the increasing advancements in video compression efficiency achieved by newer codecs such as HEVC, AV1, and VVC, and intelligent encoding strategies, as well as improved bandwidth availability,there has been a proliferation and acceptance of newer services such as Netflix, Twitch, etc. However, such higher compression efficiencies are achieved at the cost of higher complexity and encoding delay, while many applications are delay sensitive. Hence, there is a requirement for faster, more efficient codecs to achieve higher encoding efficiency without significant trade-off in terms of both co…
Staircase-like contours introduced to a video by quantization in flat areas, commonly known as banding, have been a long-standing problem in both video processing and quality assessment communities. The fact that even a relatively small change of the original pixel values can result in a strong impact on perceived quality makes banding especially difficult to be detected by objective quality metrics. In this paper, we study how banding annoyance compares to more commonly studied scaling and compression artifacts with respect to the overall perceptual quality. We further propose a simple combin…
Fuelled by the increase in popularity of virtual and augmented reality applications, point clouds have emerged as a popular 3D format for acquisition and rendering of digital humans, thanks to their versatility and real-time capabilities. Due to technological constraints and real-time rendering limitations, however, the visual quality of dynamic point cloud contents is seldom evaluated using virtual and augmented reality devices, instead relying on prerecorded videos displayed on conventional 2D screens. In this study, we evaluate how the visual quality of point clouds representing digital hum…
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Google（谷歌）
The popularity of streaming videos with live, high-action content has led to an increased interest in High Frame Rate (HFR) videos. In this work we address the problem of frame rate dependent Video Quality Assessment (VQA) when the videos to be compared have different frame rate and compression factor. The current VQA models such as VMAF have superior correlation with perceptual judgments when videos to be compared have same frame rates and contain conventional distortions such as compression, scaling etc. However this framework requires additional pre-processing step when videos with differen…
RBFIM: Perceptual Quality Assessment for Compressed Point Clouds Using Radial Basis Function Interpolation
School of Electronics and Information, Northwestern Polytechnical University（电子与信息学院，西北工业大学）；School of Engineering, Royal Melbourne Institute of Technology（工程学院，皇家墨尔本理工学院）；Department of Computer Science, City University of Hong Kong（计算机科学系，香港城市大学）；School of Telecommunication Engineering, Xidian University（电信工程学院，西安电子科技大学）
Perceptual Visual Quality Assessment: Principles, Methods, and Future Directions
Cardiff University（卡迪夫大学）；University of Klagenfurt（克拉根福大学）；Shanghai Jiao Tong University（上海交通大学）；University of Nantes（南特大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Google（谷歌）
High frame rate (HFR) videos are becoming increasingly common with the tremendous popularity of live, high-action streaming content such as sports. Although HFR contents are generally of very high quality, high bandwidth requirements make them challenging to deliver efficiently, while simultaneously maintaining their quality. To optimize trade-offs between bandwidth requirements and video quality, in terms of frame rate adaptation, it is imperative to understand the intricate relationship between frame rate and perceptual video quality. Towards advancing progression in this direction we design…
