IEEE Trans Pattern Anal Mach Intell. 2017 Jul;39(7):1482-1488. doi: 10.1109/TPAMI.2016.2582171. Epub 2016 Jun 20.
The angle between the RGBs of the measured illuminant and estimated illuminant colors-the recovery angular error-has been used to evaluate the performance of the illuminant estimation algorithms. However we noticed that this metric is not in line with how the illuminant estimates are used. Normally, the illuminant estimates are divided out' from the image to, hopefully, provide image colors that are not confounded by the color of the light. However, even though the same reproduction results the same scene might have a large range of recovery errors. In this work the scale of the problem with the recovery error is quantified. Next we propose a new metric for evaluating illuminant estimation algorithms, called the reproduction angular error, which is defined as the angle between the RGB of a white surface when the actual and estimated illuminations are divided out'. Our new metric ties algorithm performance to how the illuminant estimates are used. For a given algorithm, adopting the new reproduction angular error leads to different optimal parameters. Further the ranked list of best to worst algorithms changes when the reproduction angular is used. The importance of using an appropriate performance metric is established.
测量光源的 RGB 与估计光源颜色之间的角度——恢复角度误差——已被用于评估光源估计算法的性能。然而，我们注意到，该指标与光源估计的使用方式不一致。通常，从图像中“去除”光源估计，以希望提供不受光颜色影响的图像颜色。然而，即使相同的再现结果，同一场景可能有很大的恢复误差范围。在这项工作中，量化了恢复误差问题的规模。接下来，我们提出了一种新的评估光源估计算法的指标，称为再现角度误差，它被定义为当实际和估计的光照“去除”时，白色表面的 RGB 之间的角度。我们的新指标将算法性能与光源估计的使用方式联系起来。对于给定的算法，采用新的再现角度误差会导致不同的最佳参数。进一步，当使用再现角度误差时，最佳到最差算法的排名列表会发生变化。因此，确立了使用适当的性能指标的重要性。
