The Cube++ Illumination Estimation Dataset
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 e...
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doaj-fe1b99615ffb48a7824b35ef6488ec1f2021-03-30T04:22:42ZengIEEEIEEE Access2169-35362020-01-01822751122752710.1109/ACCESS.2020.30450669296220The Cube++ Illumination Estimation DatasetEgor Ershov0https://orcid.org/0000-0001-6797-6284Alexey Savchik1https://orcid.org/0000-0003-3035-1365Illya Semenkov2Nikola Banic3https://orcid.org/0000-0002-3900-8590Alexander Belokopytov4Daria Senshina5Karlo Koscevic6https://orcid.org/0000-0002-9691-4231Marko Subasic7https://orcid.org/0000-0002-4321-4557Sven Loncaric8https://orcid.org/0000-0002-4857-5351Russian Academy of Sciences, Institute for Information Transmission Problems, Moscow, RussiaRussian Academy of Sciences, Institute for Information Transmission Problems, Moscow, RussiaRussian Academy of Sciences, Institute for Information Transmission Problems, Moscow, RussiaGideon Brothers, Zagreb, CroatiaRussian Academy of Sciences, Institute for Information Transmission Problems, Moscow, RussiaRussian Academy of Sciences, Institute for Information Transmission Problems, Moscow, RussiaFaculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaFaculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaFaculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaComputational 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/.https://ieeexplore.ieee.org/document/9296220/Color constancydatasetillumination estimationwhite balancingmultiple illuminationmixed illumination |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Egor Ershov Alexey Savchik Illya Semenkov Nikola Banic Alexander Belokopytov Daria Senshina Karlo Koscevic Marko Subasic Sven Loncaric |
spellingShingle |
Egor Ershov Alexey Savchik Illya Semenkov Nikola Banic Alexander Belokopytov Daria Senshina Karlo Koscevic Marko Subasic Sven Loncaric The Cube++ Illumination Estimation Dataset IEEE Access Color constancy dataset illumination estimation white balancing multiple illumination mixed illumination |
author_facet |
Egor Ershov Alexey Savchik Illya Semenkov Nikola Banic Alexander Belokopytov Daria Senshina Karlo Koscevic Marko Subasic Sven Loncaric |
author_sort |
Egor Ershov |
title |
The Cube++ Illumination Estimation Dataset |
title_short |
The Cube++ Illumination Estimation Dataset |
title_full |
The Cube++ Illumination Estimation Dataset |
title_fullStr |
The Cube++ Illumination Estimation Dataset |
title_full_unstemmed |
The Cube++ Illumination Estimation Dataset |
title_sort |
cube++ illumination estimation dataset |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
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/. |
topic |
Color constancy dataset illumination estimation white balancing multiple illumination mixed illumination |
url |
https://ieeexplore.ieee.org/document/9296220/ |
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