The use of psychophysical data and models in the analysis of display system performance
Digital images and human vision
The visible differences predictor: an algorithm for the assessment of image fidelity
Digital images and human vision
Deformable Kernels for Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Strategic directions in electronic commerce and digital libraries: towards a digital agora
ACM Computing Surveys (CSUR) - Special ACM 50th-anniversary issue: strategic directions in computing research
High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting (The Morgan Kaufmann Series in Computer Graphics)
Full-Reference Image Quality Metrics: Classification and Evaluation
Foundations and Trends® in Computer Graphics and Vision
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The goal of numerous digital image processing algorithms is to reproduce an image as accurately as possible given some specific restrictions. For example, in digital image halftoning, a gray scale version of an image needs to be approximated by a high spatial resolution binary image. Likewise, lossy image compression seeks to reconstruct an image from a minimally coded description of the original. In these and many other applications, image fidelity is determined by the human observer; hence, the effectiveness of the algorithm is measured by the extent to which reproduction errors are visible. As a result, a model that predicts human perceptual sensitivity to image distortion is beneficial to both the design and evaluation of many such image processing algorithms. This summary briefly describes an extension of our work on perceptual image distortion. Our extended perceptual model accounts for: (1) contrast sensitivity as a function of spatial frequency, mean luminance and spatial extent, (2) luminance masking, and (3) contrast masking.