Green Stability Assumption: Unsupervised Learning for Statistics-Based Illumination Estimation

TL;DR

This paper introduces the green stability assumption for tuning statistics-based illumination estimation methods using only uncalibrated images, achieving accuracy comparable to calibrated methods while significantly speeding up the process.

Contribution

It proposes the green stability assumption for parameter tuning of statistics-based methods without ground-truth, improving efficiency and maintaining accuracy.

Findings

Corrected previous accuracy reports using proper cross-validation.

Green stability assumption enables parameter tuning with uncalibrated images.

Achieves similar accuracy to calibrated methods with faster processing.

Abstract

In the image processing pipeline of almost every digital camera there is a part dedicated to computational color constancy i.e. to removing the influence of illumination on the colors of the image scene. Some of the best known illumination estimation methods are the so called statistics-based methods. They are less accurate than the learning-based illumination estimation methods, but they are faster and simpler to implement in embedded systems, which is one of the reasons for their widespread usage. Although in the relevant literature it often appears as if they require no training, this is not true because they have parameter values that need to be fine-tuned in order to be more accurate. In this paper it is first shown that the accuracy of statistics-based methods reported in most papers was not obtained by means of the necessary cross-validation, but by using the whole benchmark datasets for both training and testing. After that the corrected results are given for the best known benchmark datasets. Finally, the so called green stability assumption is proposed that can be used to fine-tune the values of the parameters of the statistics-based methods by using only non-calibrated images without known ground-truth illumination. The obtained accuracy is practically the same as when using calibrated training images, but the whole process is much faster. The experimental results are presented and discussed. The source code is available at http://www.fer.unizg.hr/ipg/resources/color_constancy/.