This paper presents a new comparison framework, with the view to help researchers in selecting the most appropriate illumination compensation algorithm to serve as a preprocessing step in computer vision applications. The main objective of this framework is to reveal the positive and negative characteristics of the algorithms, rather than providing a single metric to rank their overall performance. The comparison tests, that comprise the proposed framework, aim to quantitatively evaluate the efficiency of algorithms in diminishing the effects of illumination in images. The proposed framework utilizes synthetic images, with artificial illumination degradations, which are enhanced by the tested algorithms. It represents a useful tool for the selection of illumination compensation algorithms as preprocessing in other applications, due to a) its quantitative nature, b) its easy implementation and c) its useful estimations regarding many algorithm characteristics.
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