PC-JND: Subjective Study and Dataset on Just Noticeable Difference for Point Clouds in 6DoF Virtual Reality
Chunling Fan, Yun Zhang, Dietmar Saupe, Raouf Hamzaoui, and Weisi Lin

TL;DR
This paper investigates the perceptual thresholds for point cloud distortions in VR, revealing differences between geometry and texture perception, and introduces a new dataset to aid future research in perceptual optimization.
Contribution
It presents the first study of JND for point clouds in VR and provides a publicly available dataset to support perceptual quality assessment and processing.
Findings
Texture PCJND is smaller than geometry PCJND in VR.
Colorfulness correlates with texture PCJND.
No significant correlation between point count and PCJND.
Abstract
The Just Noticeable Difference (JND) accounts for the minimum distortion at which humans can perceive a difference between a pristine stimulus and its distorted version. The JND concept has been widely applied in visual signal processing tasks, including coding, transmission, rendering, and quality assessment, to optimize human-centric media experiences. A point cloud is a mainstream volumetric data representation consisting of both geometry information and attributes (e.g. color). Point clouds are used for advanced immersive 3D media such as Virtual Reality (VR). However, the JND characteristics of viewing point clouds in VR have not been explored before. In this paper, we study the point cloud-wise JND (PCJND) characteristics in a Six Degrees of Freedom (6DoF) VR environment using a head-mounted display. Our findings reveal that the texture PCJND of human eyes is smaller than the…
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