Structure and color based comparison of halftone images

A combined similarity metric for analysis of image quality is presented, which allows comparisons of halftone images based on their color difference and structure similarity. Color comparison is based on the I*color metric [2], which was developed in the context of photographic image persistence. Structure similarity SSIM [1], initially designed for quality estimation in the context of image and video coding, characterizes the achromatic channel and refers to average luminance, average gradient and covariance (i.e. structure) calculated within a pre-defined neighborhood of each pixel. The two images to be compared must be given in terms of XYZ. After color opponent processing, they are spatially filtered according to the viewing distance with a spatial human visual observer model (HVOM), that takes different blurring of achromatic and chromatic channels into account (CSF's for AC1C2). In contrast to the original SSIM approach, CIELAB L* or the A channel of the AC1C2 color space (see [3]) are used for the achromatic channel to already account for the non-linearity of luminance perception. Several examples of image comparison of simulated halftone prints are presented. Introduction A quantitative similarity comparison of halftone printed pictorial images is challenging due to their discrete structure as a result of the printing method, due to the changes that printouts undergo in different applications (drying, fading etc.) and due to viewing circumstances (viewing distance etc.). A manifold of comparison methods and combinations of these methods with different weighting factors have been proposed for various applications over the last decades, spanning from global image quality measures [6] to image-wise comparison, most of them being based on a pixel-wise or histogram comparison. Continuous work of CIE [8, 9] resulted in improved color appearance distance metrics for comparably color patches, and the most recent one, CIE ∆E2000, is widely used and generally accepted as a standard providing satisfactory results for many practical needs, like color rendering benchmarks. Detail rendition aspects are addressed by spatial extension of CIELAB or more sophisticated multi-scale observer models. These methods were applied to benchmark image compression algorithms and virtual rendering. When it comes to halftone image comparisons, color differences are usually computed on the color palette of each image [4]. The drawback of CIE ∆E2000 is given by its applicability to small changes in color and tone reproduction only. In certain applications, however, there is a need of quantifying more pronounced color changes, even falsely encoded colors. The paper of McCormick-Goodhart et al. [2] offers an interesting and more extensive debate on this problem in the context of image permanence for photographs. The authors propose a computational model for estimating ‘the retained image appearance’ of photographs, called I* model. That paper also stresses the importance of a metric that directly provides results on specific images rather than being tied to a certain test target, like a predefined array of color patches. Also, the authors argue that the color information in an image (hue and chroma) is supplemental to the black-and-white information (lightness and contrast), and they therefore design the I* model to analyze hue and chroma information separately from lightness and contrast information. I*BW accounts for changes in achromatic contrast and luminance expressed in L*, while the I*color metric accounts for changes in chroma and hue. The result of the two analyses can be combined in an overall “figure of merit”, that combines the two with a weight. Wang et al. [1] have presented a method for the estimation of similarity between images with focus on the achromatic channel, the structure similarity metric SSIM: it measures similarity of images in terms of luminance, gradient and structure. This method is a full-reference quality assessment method, initially designed for quality estimation in the context of image and video coding. The main advantage of SSIM over I*BW is the introduction of structure information and a more flexible use of the pixel neighborhood. In this paper we propose a combination of the I*color difference metric and the SSIM structure similarity metrics (achromatic channel), to which a human visual observer model can be added to obtain perceived image difference maps, that together depict the most relevant dimensions of image appearance, namely color, luminance, contrast and structure. With the individual difference maps and similarity scores at hands, still correlation with psychophysical scores need to be worked out, either using average difference measures or other percentiles to resemble “peak-picking” [7].