research area Computer Vision, research area Methods and AlgorithmsWorkshop at CVPR
content type paperpublished May 2025

What Makes for a Good Stereoscopic Image?

AuthorsNetanel Y. Tamir†‡, Shir Amir, Ranel Itzhaky, Noam Atia†, Shobhita Sundaram§, Stephanie Fu¶, Ron Sokolovsky, Phillip Isola§, Tali Dekel‡, Richard Zhang¶, Miriam Farber

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This paper was accepted at the CV4Metaverse Workshop at CVPR 2025.

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.

  • † Work done while at Apple
  • ‡ Weizmann Institute of Science
  • § Massachusetts Institute of Technology
  • ¶ Independent researcher

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