A Curious Problem with Using the Colour Checker Dataset for Illuminant Estimation
A Curious Problem with Using the Colour Checker Dataset for Illuminant Estimation
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DOI：
10.2352/issn.2169-2629.2017.25.64
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发表时间：
2017-09
影响因子：
4.2
通讯作者：
G. Finlayson;Ghalia Hemrit;A. Gijsenij;Peter Gehler
中科院分区：
文献类型：
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作者：
G. Finlayson;Ghalia Hemrit;A. Gijsenij;Peter Gehler
在光源估计中，我们尝试估计灯光的RGB。然后，我们在图像上使用该估计值来校正光线的颜色偏差。照度估计是所有摄像机复制流水线的重要组成部分。照度估计算法的工作效果取决于它对地面真实照度颜色的预测程度。通常，地面真实情况是放置在场景中的白色曲面的RGB。对一大组图像计算估计误差，然后根据不同算法的平均估计性能对不同算法进行排名。也许在照度估算中使用最广泛的公开可用的数据集是Gehler‘s Colour Checker Set，它由史和芬特重新处理。这一图像集包括568张典型日常场景的图像。奇怪的是，我们发现了三个不同的基础事实，为Shif-Fant颜色检查器图像集。在本文中，我们调查了采用一个基本事实而不是另一个基本事实是否会导致照度估计算法的不同排名。我们发现，根据所使用的基本事实，不同算法的排名可能会发生变化，有时甚至是戏剧性的。事实上，最近在照度估计方面取得的许多“进步”完全有可能实现，因为作者已经转向使用基本事实，在那里有可能获得更好的估计性能。
In illuminant estimation, we attempt to estimate the RGB of the light. We then use this estimate on an image to correct for the light's colour bias. Illuminant estimation is an essential component of all camera reproduction pipelines. How well an illuminant estimation algorithm works is determined by how well it predicts the ground truth illuminant colour. Typically, the ground truth is the RGB of a white surface placed in a scene. Over a large set of images an estimation error is calculated and different algorithms are then ranked according to their average estimation performance. Perhaps the most widely used publically available dataset used in illuminant estimation is Gehler's Colour Checker set that was reprocessed by Shi and Funt. This image set comprises 568 images of typical everyday scenes. Curiously, we have found three different ground truths for the Shi-Funt Colour Checker image set. In this paper, we investigate whether adopting one ground truth over another results in different rankings of illuminant estimation algorithms. We find that, depending on the ground truth used, the ranking of different algorithms can change, and sometimes dramatically. Indeed, it is entirely possible that much of the recent 'advances' made in illuminant estimation were achieved because authors have switched to using a ground truth where better estimation performance is possible.