面向多媒体主观质量投票、均值与方差的混合行为投票模型
A Mixed-Behavior Vote Model for Multimedia Subjective Quality Votes, Means, and Variances
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中文总结 AI 辅助
该研究针对多媒体主观质量投票，提出了单峰方差区域及混合行为投票模型，解决抛物线方差模型违反可允许区域的问题，通过16个多模态数据集验证了模型的有效性。
中文摘要 AI 辅助
主观测试投票方差与投票均值（即MOS）之间的关系已得到充分研究，此前已定义了数学上可允许的投票方差区域。我们提出了一种缩减后的可允许方差区域，称为单峰方差区域（Unimodal Variance Region，UVR），该区域能更好地描述多媒体的真实主观评分行为。此外，主观投票方差常被建模为抛物线形式，我们解释了在实际应用中，抛物线模型常违反方差-MOS平面内的可允许区域，因此提出了符合该可允许区域的替代方案。我们还提出了一种参数化随机过程来建模投票，该过程混合了多种投票过程，可在任意期望MOS下，生成UVR内符合实际的投票方差范围。此过程受众多主观测试中观察到的投票行为启发，且与该行为相符。通过建模主观实验中的投票方差，该投票模型可为给定实验中观察到的投票行为提供额外的可解释性见解。我们展示了涵盖语音、图像和视频主观质量实验的16个数据集的示例结果。
英文摘要
The relationship between subjective test vote variance and vote mean (or MOS) is well-studied, and the mathematically admissible vote variance region has been previously defined. We propose a reduced admissible variance region called the Unimodal Variance Region (UVR) that better describes real subjective rating behavior of multimedia. Further, subjective vote variance is often modeled as parabolic. We explain that, in practice, the parabolic model often violates the admissible region in the variance vs. MOS plane and we propose alternatives that respect the admissible region. We also present a parametrized random process to model votes that mixes voting processes and produces a realistic range of vote variances within the UVR at any desired MOS. This process was inspired by and comports with voting behavior that is observed in many subjective tests. By modeling vote variance from a subjective experiment, this vote model offers additional interpretable insights into voting behavior observed in a given experiment. We present example results from 16 datasets spanning speech, image, and video subjective quality experiments.
