What Makes for a Good Stereoscopic Image?

TL;DR

This paper introduces SCOPE, a comprehensive stereoscopic image dataset with preference annotations, and iSQoE, a new model for assessing stereoscopic quality that better aligns with human preferences, advancing VR experience evaluation.

Contribution

The paper presents SCOPE, a diverse stereoscopic image dataset with preference labels, and iSQoE, a novel quality assessment model trained on this dataset, improving correlation with human preferences.

Findings

User preferences are consistent across different VR headsets.

iSQoE outperforms existing metrics in predicting human preferences.

The dataset includes a wide range of perceptual distortions and artifacts.

Abstract

With rapid advancements in virtual reality (VR) headsets, effectively measuring stereoscopic quality of experience (SQoE) has become essential for delivering immersive and comfortable 3D experiences. However, most existing stereo metrics focus on isolated aspects of the viewing experience such as visual discomfort or image quality, and have traditionally faced data limitations. To address these gaps, we present SCOPE (Stereoscopic COntent Preference Evaluation), a new dataset comprised of real and synthetic stereoscopic images featuring a wide range of common perceptual distortions and artifacts. The dataset is labeled with preference annotations collected on a VR headset, with our findings indicating a notable degree of consistency in user preferences across different headsets. Additionally, we present iSQoE, a new model for stereo quality of experience assessment trained on our dataset. We show that iSQoE aligns better with human preferences than existing methods when comparing mono-to-stereo conversion methods.