What Makes for a Good Stereoscopic Image?
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912306247499776 |
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| author | Tamir, Netanel Y. Amir, Shir Itzhaky, Ranel Atia, Noam Sundaram, Shobhita Fu, Stephanie Sokolovsky, Ron Isola, Phillip Dekel, Tali Zhang, Richard Farber, Miriam |
| author_facet | Tamir, Netanel Y. Amir, Shir Itzhaky, Ranel Atia, Noam Sundaram, Shobhita Fu, Stephanie Sokolovsky, Ron Isola, Phillip Dekel, Tali Zhang, Richard Farber, Miriam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_21127 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | What Makes for a Good Stereoscopic Image? Tamir, Netanel Y. Amir, Shir Itzhaky, Ranel Atia, Noam Sundaram, Shobhita Fu, Stephanie Sokolovsky, Ron Isola, Phillip Dekel, Tali Zhang, Richard Farber, Miriam Computer Vision and Pattern Recognition 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. |
| title | What Makes for a Good Stereoscopic Image? |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.21127 |