Section on Human Vision and Electronic Imaging

This collection of papers commemorates the tenth anniversary of the IS&T/ SPIE Conference on Human Vision and Electronic Imaging. These papers represent major trends in the conference and demonstrate the interplay between real-world imaging applications and vision research. In the first paper (Rogowitz, Pappas, and Allebach), we present an overview of this field and provide a context for the papers that follow. We use the metaphor of a food chain to draw attention to the many levels of human visual processing and how they have influenced different imaging applications. In this scheme, low level vision is concerned with the detection and recognition of visual patterns by simple, mainly linear mechanisms, mediated by retinal or striate cortex filters. Moving up the food chain, more complex, often non-linear processes are posited to model the perception of more complex images and objects, and explore higher-level cortical functions such as visual attention and pattern recognition. At the top of the food chain are those visual tasks which involve judgments about very rich environments, aesthetic judgments and emotional responses. In this view, the role of the human observer in imaging systems cannot be modeled as a simple function, but instead, is a complex set of visual, perceptual, and cognitive behaviors. Understanding these behaviors, and applying them appropriately, is the focus of the Conference on Human Vision and Electronic Imaging and of this special section. One of the important issues in the design of imaging systems is the most efficient use of the available transmission bandwidth and storage capacity, while preserving the best image quality. The next three papers demonstrate the influence of low-level vision models on the quantitative evaluation of image quality and on the development of image compression techniques. These approaches are based on detectability and evaluation of image artifacts. Watson, Hu, and McGowan’s paper derives an objective metric for the evaluation of video image quality, especially for video compression. It makes use of spatiotemporal models of human perception. Daly, Matthews, and RibasCorbera use models of foveal eccentricity to allocate most of the limited transmission bandwidth to the face region for video conferencing applications. In the next paper, de Ridder examines the influence of judgment strategies, and in particular the composition of the stimulus set and the instructions, on the outcome of subjective experiments for the evaluation of image quality. With the next three papers, we consider more complex visual images, more complex visual tasks, and higher level approaches to modeling their perception. An important new approach in vision research has been to understand the relationship between the statistics of natural images and the neural mechanisms that have evolved to process them. In their paper, Zetzsche and Krieger argue that the way the human visual system exploits statistical redundancies in natural images is highly non-linear and involves higherorder statistics. The authors model the perception of complex visual images and explore the implications of their findings for imaging technology. The MacLin and Webster paper provides a vivid example of how exquisitely complex human perception really is. They use a standard experimental paradigm in psychophysics, the adaptation experiment, to demonstrate the ability of the human visual system to adapt to very complex spatial perturbations. Using faces, they find that when observers spend time viewing spatial distortions in a face (e.g., widened distance between the eyes), they perceive nondistorted faces as having been distorted in the opposite direction (e.g., narrowing distance between the eyes). This suggests the existence of very high-level image processing mechanisms, or sets of mechanisms, in human vision, whose response(s) can be weakened through this adaptation process. In exploring the role of higherlevel, non-linear, and more sophisticated mechanisms in human vision, it is always important to ask whether complex behaviors might not be sufficiently modeled by lower-level, less