New Image Quality Assessment Algorithms for CFA Demosaicing

To address the frequent lack of a reference image or ground truth when performance testing Bayer pattern color filter array demosaicing algorithms, we propose two new no-reference quality assessment algorithms. These algorithms provide a relative comparison of two demosaicing algorithms by measuring the presence of two common artifacts, zippering and false coloring, in their output images. The first algorithm, the edge slope measure, tests the overall sharpness of each of the three color channels, thus estimating the relative edge reconstruction accuracy of each demosaicing algorithm. The second algorithm, the false color measure, estimates deviations from the established constant color difference image model and performs on green-red and green-blue color difference planes, therefore estimating the red and blue channel reconstruction of each demosaicing algorithm. We evaluate and rank common demosaicing algorithms using these new algorithms. Furthermore, we present real image examples for subjective evaluation to justify the rankings suggested by the new quality assessment algorithms.