What Makes for a Good Stereoscopic Image?
This addresses the need for improved SQoE assessment in VR to enhance immersive experiences, though it is incremental as it builds on existing stereo metrics with new data and a model.
The paper tackles the problem of measuring stereoscopic quality of experience (SQoE) for VR by introducing SCOPE, a dataset with preference-labeled stereoscopic images, and iSQoE, a model that better aligns with human preferences than existing methods, showing consistency across headsets.
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.