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Scene-and-Process-Dependent Spatial Image Quality Metrics

机译：取决于场景和过程的空间图像质量指标

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Spatial image quality metrics designed for camera systems generally employ the Modulation Transfer Function (MTF), the Noise Power Spectrum (NPS) and a visual contrast detection model. Prior art indicates that scene-dependent characteristics of non-linear, content-aware image processing are unaccounted for by MTFs and NPSs measured by traditional methods. The authors present two novel metrics: the log Noise Equivalent Quanta (log NEQ) and Visual log NEQ. They both employ Scene-and-Process-Dependent MTF (SPD-MTF) and NPS (SPD-NPS) measures, which account for signal transfer and noise scene dependency, respectively. The authors also investigate implementing contrast detection and discrimination models that account for scene-dependent visual masking. Also, three leading camera metrics are revised to use the above scene-dependent measures. All metrics are validated by examining correlations with the perceived quality of images produced by simulated camera pipelines. Metric accuracy improved consistently when the SPD-MTFs and SPD-NPSs were implemented. The novel metrics outperformed existing metrics of the same genre. (C) 2019 Society for Imaging Science and Technology.
机译：为相机系统设计的空间图像质量度量通常采用调制传递函数（MTF），噪声功率谱（NPS）和视觉对比度检测模型。现有技术表明，通过传统方法测量的MTF和NPS无法解决非线性的，可感知内容的图像处理的场景相关特征。作者提出了两个新颖的指标：对数噪声等效量（log NEQ）和可视对数NEQ。它们都采用依赖于场景和过程的MTF（SPD-MTF）和NPS（SPD-NPS）措施，它们分别考虑了信号传输和噪声场景依赖性。作者还研究了实现对比检测和辨别模型的方法，这些模型可以解决场景依赖的视觉蒙版问题。另外，对三个领先的相机指标进行了修订，以使用上述与场景有关的度量。通过检查与模拟相机管线产生的图像的感知质量的相关性，可以验证所有指标。实施SPD-MTF和SPD-NPS时，公制精度不断提高。新颖的指标优于相同类型的现有指标。 （C）2019影像科学与技术学会。

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