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Introduction: Special Section on Image Quality Assessment Methods for the Design and Optimization of Display Systems

One of the most complex challenges that our community faces is at the interface between the device and the viewer. It is a relatively straightforward task for our engineers and scientists to design a device with an improved aspect of display performance, such as contrast or angular distribution of the luminance emissions. There are proven methods to quantify those characteristics in the laboratory. We may argue about different definitions of the parameters and different metrological and instrumentation approaches, but, in the end, we can resolve differences and reach consensus with the help of precise descriptions of experimental protocols, clearly documented in the open literature. Unfortunately, assessment methods that suggest the benefits of device improvements with respect to human observers often lack the solidity and precise reproducibility of the physical measurements. The connections between the physical and psycho-physical experiments always entail a large degree of variability in the observer’s response that complicates the studies and hinders definitive conclusions. It is in this framework that we engaged the Journal of the SID to devote a Special Section to the topic of “Image Quality Assessment Methods for the Design and Optimization of Display Systems.” Many works on this topical area lack fundamental definitions of essential terms. In imaging science, image quality has many meanings. Barrett and Myers [Foundations of Image Science (John Wiley and Sons, New York, 2006)] describe several approaches to the assessment of image quality. In the following, we will briefly introduce the different approaches from the perspective of the assessment of display quality. The simplest approach relies on a viewer’s subjective assessment regarding the quality of a display to obtain ratings or rankings of devices or device improvements. The response of the viewers can be predicted by models that incorporate aspects of device performance chosen to represent the needs of the particular application. The validity of this method is often questioned due to the uncertain relationship between viewer’s preference and objective quality and is always limited to the set of viewers and images used in the study. With this approach, generalizations are rarely justifiable. The ability of a device to present a displayed image that closely reproduces a given image or real-world scene can be used to measure the fidelity of the system. Fidelity measures are sensitive to the definition of the metric used (mean-square-error is a common one), to the gray-scale representation of the display device, and to distortion artifacts that can affect fidelity dramatically without a significant effect on perceived quality. Moreover, the fidelity approach does not weigh the differences between the image or scene and the displayed image with respect to their perceptual or contextual significance or their screen location, potentially pointing to misleading conclusions. Fidelity approaches need to address the complication that images are discrete representations of continuous objects and scenes and that all display devices represent the images as continuous luminance emissions. It is not obvious how fidelity measures can be defined when a discrete set of pixel values is represented as a continuous map. If instead of equal weighting of the image differences, we use weights associated with the response of the human visual system, we end up with a perceptual-difference assessment method. This third approach relies on the assumption that the fidelity needs to be achieved only to the perceptual limits of the viewer. In this approach, the difference between the displayed and the original image or scene is qualified by the concept of just-noticeable difference (JND), which corresponds to a given probability that the observer would detect the difference. If the difference is below the “threshold” for detection, image quality will not be affected. The JND approach is a modified fidelity method with perceptual weights. Finally, an objective assessment method exists that quantifies image quality based on four key elements: the visual task involved in the application, a description of the images to be displayed, the characteristics of the viewer, and a figure of merit that quantifies the performance of the observer. The visual task can be as precisely defined as the detection of a target in a cluttered background, or as loosely defined as the identification of skin tones in a human face. The images to be displayed can be characterized statistically to represent a particular set of scenes typical for the application. Alternatively, we may be interested in investigating the effect of the display device on scenes of a certain nature, stressing the ability of the device to present such type of scenes to the viewer. An objective method for the quantification of image quality also needs to precisely define the observer (human or computational model) and study protocol used in the evaluation. Last, a figure of merit needs to be defined that unequivocally indicates how well the observer performs the visual task. This Special Section contains a broad collection of papers addressing all of the above-mentioned aspects, including physical measurements, human observer studies, and computational models of human viewers. All of these aspects contribute to our understanding of the display image quality (see Fig. 1). Fine examples of investigations into experimental measurement methods include evaluations of resolution, noise, and angular distribution of display emissions.