Abstract
Computational color constancy has the important task of reducing the influence of the scene illumination on the object colors. As such, it is an essential part of the image processing pipelines of most digital cameras. One of the important parts of the computational color constancy is illumination estimation, i.e. estimating the illumination color. When an illumination estimation method is proposed, its accuracy is usually reported by providing the values of error metrics obtained on the images of publicly available datasets. However, over time it has been shown that many of these datasets have problems such as too few images, inappropriate image quality, lack of scene diversity, absence of version tracking, violation of various assumptions, GDPR regulation violation, lack of additional shooting procedure info, etc. In this paper, a new illumination estimation dataset is proposed that aims to alleviate many of the mentioned problems and to help the illumination estimation research. It consists of 4890 images with known illumination colors as well as with additional semantic data that can further make the learning process more accurate. Due to the usage of the SpyderCube color target, for every image there are two ground-truth illumination records covering different directions. Because of that, the dataset can be used for training and testing of methods that perform single or two-illuminant estimation. This makes it superior to many similar existing datasets. The datasets, it's smaller version SimpleCube++, and the accompanying code are available at https://github.com/Visillect/CubePlusPlus/.
Highlights
T HE human visual system is able, in some conditions, to recognize colors despite the influence of the illumination on their appearance through the ability known as color constancy [1]
In this paper a new illumination estimation dataset is proposed that aims to alleviate many of the mentioned problems and to help the illumination estimation research
For all well-known datasets the ground-truth illumination used during the test phase is publicly available and the actual error statistics calculation is usually performed by the authors themselves and published in their papers
Summary
T HE human visual system is able, in some conditions, to recognize colors despite the influence of the illumination on their appearance through the ability known as color constancy [1]. It is not yet fully understood how this ability functions and it is not possible to directly model it. Various computational color constancy methods are used in the pipelines of digital cameras. They are usually designed to first identify the chromaticity of the light source and to remove its influence on the scene. For both of these tasks the commonly used image formation model that
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