Exploiting Color Strength to Improve Color Correction
Color information is an important feature for many vision algorithms including color correction, image retrieval and tracking. In this paper, we study the limitations of color measurement accuracy and explore how this information can be used to improve the performance of color correction. In particular, we show that a strong correlation exists between the error in hue measurements on one hand and saturation and intensity on the other hand. We introduce the notion of color strength, which is a combination of saturation and intensity information to determine when hue information in a scene is reliable. We verify the predictive capability of this model on two different datasets with ground truth color information. Further, we show how color strength information can be used to significantly improve color correction accuracy for the 11K real-world SFU gray ball dataset.