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<p>User-generated 360°&#xa0;video content is becoming increasingly popular due to the availability of low-cost acquisition devices and its inherently immersive nature. To facilitate research and development in this area, we introduce RBD-360, a dataset designed explicitly for quality assessment of user-generated 360°&#xa0;videos that includes various distortions typical of non-professional settings. RBD-360 features a collection of randomly captured source videos across multiple categories, recorded in three different environments: indoor, outdoor during the day, and outdoor at night. The impaired video sequences are created using three standard codecs—H.264, H.265, and VP9 at varying bitrates and quantization parameters. To evaluate subjective video quality, we employed an HMD-based Modified Absolute Category Rating protocol, along with assessments from the Simulator Sickness Questionnaire. This helps capture time-dependent viewer discomfort according to ITU-T P.919 recommendations. We implement benchmark quality assessment metrics on the RBD-360 dataset and report the results. The dataset is publicly available for testing quality assessment models tailored for user-generated videos at <a href="https://github.com/manav2701/RBD-360">https://github.com/manav2701/RBD-360</a>.</p>

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Realistic benchmark RBD360 dataset for quality assessment of random user generated 360° videos

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月16日

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  • Manav Arun Mehta,
  • Pramit Mazumdar,
  • Kalyan Chatterjee,
  • Sweta Dey,
  • Mudassir Khan,
  • Raja Shekar Kadurka

摘要

User-generated 360° video content is becoming increasingly popular due to the availability of low-cost acquisition devices and its inherently immersive nature. To facilitate research and development in this area, we introduce RBD-360, a dataset designed explicitly for quality assessment of user-generated 360° videos that includes various distortions typical of non-professional settings. RBD-360 features a collection of randomly captured source videos across multiple categories, recorded in three different environments: indoor, outdoor during the day, and outdoor at night. The impaired video sequences are created using three standard codecs—H.264, H.265, and VP9 at varying bitrates and quantization parameters. To evaluate subjective video quality, we employed an HMD-based Modified Absolute Category Rating protocol, along with assessments from the Simulator Sickness Questionnaire. This helps capture time-dependent viewer discomfort according to ITU-T P.919 recommendations. We implement benchmark quality assessment metrics on the RBD-360 dataset and report the results. The dataset is publicly available for testing quality assessment models tailored for user-generated videos at https://github.com/manav2701/RBD-360.