The widespread adoption of smartphones with high-resolution cameras has driven a surge in image capture, particularly for selfies, food, and landscapes, which dominate social media. Efficient image compression is essential to reduce storage and transmission requirements while maintaining visual quality. Traditional methods like JPEG and JPEG2000 have reached their limits, making learning-based image compression (LIC) a promising alternative. However, most LIC models, such as SegPIC [2], are trained on general-purpose datasets like COCO, limiting their effectiveness for smartphone-specific content.

The ColorChecker dataset is one of the most widely used image sets for evaluating and ranking illuminant estimation algorithms. However, this single set of images has at least 3 different sets of ground-truth (i.e., correct answers) associated with it. In the literature it is often asserted that one algorithm is better than another when the algorithms in question have been tuned and tested with the different ground-truths. In this short correspondence we present some of the background as to why the 3 existing ground-truths are different and go on to make a new single and recommended set of correct answers. Experiments reinforce the importance of this work in that we show that the total ordering of a set of algorithms may be reversed depending on whether we use the new or legacy ground-truth data.