As computers proliferate and as the field of computer graphics matures, it has become increasingly important for computer scientists to understand how users perceive and interpret computer graphics. Experimental Design: From User Studies to Psychophysics is an accessible introduction to psychological experiments and experimental design, covering the major components in the design, execution, and analysis of perceptual studies. The book begins with an introduction to the concepts central to designing and understanding experiments, including developing a research question, setting conditions and controls, and balancing specificity with generality. The book then explores in detail a number of types of experimental tasks: free description, rating scales, forced-choice, specialized multiple choice, and real-world tasks as well as physiological studies. It discusses the advantages and disadvantages of each type and provides examples of that type of experiment from the authors' own work. The book also covers stimulus-related issues, including popular stimulus resources. It concludes with a thorough examination of statistical techniques for analyzing results, including methods specific to individual tasks.

As computers proliferate and as the field of computer graphics matures, it has become increasingly important for computer scientists to understand how users perceive and interpret computer graphics. Experimental Design: From User Studies to Psychophysics is an accessible introduction to psychological experiments and experimental design, covering the major components in the design, execution, and analysis of perceptual studies.

The book begins with an introduction to the concepts central to designing and understanding experiments, including developing a research question, setting conditions and controls, and balancing specificity with generality. The book then explores in detail a number of types of experimental tasks: free description, rating scales, forced-choice, specialized multiple choice, and real-world tasks as well as physiological studies. It discusses the advantages and disadvantages of each type and provides examples of that type of experiment from the authors own work. The book also covers stimulus-related issues, including popular stimulus resources. It concludes with a thorough examination of statistical techniques for analyzing results, including methods specific to individual tasks.

To achieve the complex task of interpreting what we see, our brains rely on statistical regularities and patterns in visual data. Knowledge of these regularities can also be considerably useful in visual computing disciplines, such as computer vision, computer graphics, and image processing. The field of natural image statistics studies the regularities to exploit their potential and better understand human vision. With numerous color figures throughout, Image Statistics in Visual Computing covers all aspects of natural image statistics, from data collection to analysis to applications in computer graphics, computational photography, image processing, and art.

The authors keep the material accessible, providing mathematical definitions where appropriate to help readers understand the transforms that highlight statistical regularities present in images. The book also describes patterns that arise once the images are transformed and gives examples of applications that have successfully used statistical regularities. Numerous references enable readers to easily look up more information about a specific concept or application. A supporting website also offers additional information, including descriptions of various image databases suitable for statistics.

Collecting state-of-the-art, interdisciplinary knowledge in one source, this book explores the relation of natural image statistics to human vision and shows how natural image statistics can be applied to visual computing. It encourages readers in both academic and industrial settings to develop novel insights and applications in all disciplines that relate to visual computing.