Hybrid model of alternating least squares and root polynomial technique for color correction

Abstract

Colour correction is an image-altering technique that modifies image color in such a way that it matches a reference image. Various researchers have already proposed many approaches. However, those models have been unable to reduce color errors between two images, which results in inefficiency and poor-quality images. This research paper presents an effective and improved color correction model wherein Alternate Least Square (ALS), and Root Polynomial (RP) are used together. The main objective of the proposed model is to reduce the error between a reference image and a target image to make it look realistic. The proposed model used the Amsterdam Library of Object Images (ALOI) to achieve this objective, which contains a picture of single objects captured under various illumination angles and colors. After this, a hybrid ALS+RP color correction technique is implemented on the dataset image that fixes its color for the reference image. The target image is then converted into three color models i.e., LAB, LUV, and RGB, into XYZ format. Finally, the color difference between a reference image and a target image is observed by calculating values for parameters like Mean, Median, 95% quantile, and maximum error. The effectiveness of the suggested hybrid color correction approach is assessed and validated in MATLAB software for each color model. Through extensive experiments, it is observed that the proposed hybrid model yields the least errors for the RGB color model. This is followed up by LUV and then LAB to prove its supremacy over other models.