Color image fidelity metrics evaluated using image distortion maps

Several color image delity metrics are evaluated by comparing the metric predictions to empirical measurements. Subjects examined image pairs consisting of an original and a reproduction. They marked locations on the reproduction that differed detectably from the original. We refer to the distribution of error marks by the subjects as image distortion maps. The empirically obtained image distortion maps are compared to the predicited visible di erence calculated using (1) the widely used root mean square error (pointby-point RMS) computed in uncalibrated RGB values, (2) the point-by-point CIELAB E94 values (CIE, 1994), and (3) S-CIELAB E94, a spatial extension of CIELAB E metric. The uncalibrated RMS metric did not predict the perceptual image distortion data well. The point-by-point CIELAB E94 metric provided better predictions, and the S-CIELAB metric, which incorporated the spatial color sensitivity of the eye, gave the most accurate predictions. None of the metrics provided an excellent t to the data. Image areas with poor predictions were concentrated in regions containing large negative local contrast. When these areas were excluded from our data analysis, both S-CIELAB and CIELAB predictions had much better agreement with the perceptual data. This suggests that the next step in improving color image delity metrics is to re-de ne color di erence formula such as CIELAB E94 in terms of local contrast. Measurement of perceptual image delity is an important issue in digital image Preprint submitted to Elsevier Preprint 23 July 1998 reproduction. When a reproduction appears identical to the original, we say it has perfect perceptual image delity. In current imaging applications, printed and displayed reproductions have visible distortions when compared to the original. To develop methods for improving image delity, it is useful to have metrics to measure delity. These metrics must account for the characteristics of the human visual system. Metrics for predicting the visibility of color changes of large uniform targets have been used widely to describe tolerances for color reproduction of large samples in the paint and dye industry. The CIELAB metric is a standard that speci es how to transform physical image measurements into perceptual di erences. The metric was derived from perceptual measurements of color discrimination of large uniform targets. Though not perfect, the metric has been in use for twenty years, and it has served as a satisfactory tool for measuring perceptual di erence between large uniform patches of colors. A modi cation of the E formula was released by CIE in 1994 based on new experimental data. The new formula was found to predict color di erence slightly better than the old formula [1]. Hence, in this paper we will use the CIE94 formula to calculate E values. The CIELAB metric is not suited for image delity. Many studies have found that color discrimination and appearance depend on spatial pattern of the images [2{7]. For example, the human visual system is not as sensitive to color di erences in ne details as compared to large patches, yet the CIELAB color metric will predict the same perceptual di erence for the two cases since there is no spatial variable in the CIELAB transformation. In a spatial extension of CIELAB, named S-CIELAB [8], the spatial-color sensitivity of the human eye is included in the metric. The S-CIELAB metric incorporates the di erent spatial sensitivities of the three opponent color channels by adding a spatial pre-processing step before the standard CIELAB E calculation. This spatial extension is designed to accounts for human spatialcolor sensitivity and thus improve the performance of the CIELAB E metric for patterned targets. To test how well the S-CIELAB metric predicts image delity of natural images, we measured perceptual image distortion for a set of color images. These images were displayed on a CRT monitor, and reproduced using either (a) simple halftoning algorithm or (b) a simple image compression algorithm [9]. In this paper we compare how well several color metrics predict the perceptual image delity.