Theoretically, there can be a compromise between color and spatial image quality for multispectral imaging: Increasing the number of channels increases spectral and colorimetric accuracy, that is, color quality increases; decreasing the number of channels reduces image noise and other spatial artifacts, that is, spatial image quality increases. Two paired comparison psychophysical experiments were performed to scale color and spatial image quality in order to better understand this compromise. Test targets, a watercolor painting, and several dioramas were imaged using three-, six-, and 31-channel image acquisition systems. One of the three-channel systems was a professional grade trichromatic digital camera; the other systems used the identical research grade sensor. For the six- and 31-channel images, both direct pseudoinverse based transformations and the use of principal component analysis were used to convert from digital to spectral data. The spectral data were used to render colorimetric images. Pseudoinverse transformations were used to convert the three-channel images to colorimetry. Twenty-seven observers judged, successively, color and spatial image quality of colorimetric images rendered for an LCD display compared with objects viewed in a light booth.The targets were evaluated under simulated daylight (6800K) and incandescent (2700K) illumination and the visual data were transformed to quality scales using Thurstone's law of comparative judgments, Case V. The first experiment evaluated color image quality. Under simulated daylight, the subjects judged all of the images to have the same color accuracy, except the professional camera image that was significantly worse. Under incandescent illumination, all the images, including the professional camera, had equivalent performance. The second experiment evaluated spatial image quality. The results of this experiment were highly target dependent. A subsequent image registration experiment showed that the results of the spatial image quality experiment were affected by image registration to some degree. For both experiments, there was high observer uncertainty and poor data normality. Dual scaling and a graphical analysis of observer response data were used as alternate techniques to Thurstone's Law. These techniques yielded similar results to the Thurstone-based quality scales. The uncertainty was caused by insufficient ambiguity between images. A simultaneous analysis of the color and spatial image quality results for the research grade sensor indicated that the most preferred image types were the 31-channel images. Thus, it is possible for multispectral images with many channels to achieve similar color and spatial image quality to systems with just a few channels.The theoretical compromise between color and spatial image quality as the number of channels increased was not observed under these experimental conditions.