Overview
- Enriches understanding of Image Quality Assessment
- Explains how computer-generated images are rendered and how this introduces visual noise
- Demonstrates the use of learning machines and fuzzy-sets as full-reference, reduced-reference and no-reference metrics
- Illustrates the complete process of Image Quality Assessment for computer-generated images using real experiments
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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Table of contents (7 chapters)
Keywords
About this book
Image Quality Assessment is well-known for measuring the perceived image degradation of natural scene images but is still an emerging topic for computer-generated images. This book addresses this problem and presents recent advances based on soft computing. It is aimed at students, practitioners and researchers in the field of image processing and related areas such as computer graphics and visualization.
In this book, we first clarify the differences between natural scene images and computer-generated images, and address the problem of Image Quality Assessment (IQA) by focusing on the visual perception of noise. Rather than using known perceptual models, we first investigate the use of soft computing approaches, classically used in Artificial Intelligence, as full-reference and reduced-reference metrics. Thus, by creating Learning Machines, such as SVMs and RVMs, we can assess the perceptual quality of a computer-generated image. We also investigate the use of interval-valuedfuzzy sets as a no-reference metric.These approaches are treated both theoretically and practically, for the complete process of IQA. The learning step is performed using a database built from experiments with human users and the resulting models can be used for any image computed with a stochastic rendering algorithm. This can be useful for detecting the visual convergence of the different parts of an image during the rendering process, and thus to optimize the computation. These models can also be extended to other applications that handle complex models, in the fields of signal processing and image processing.
Authors and Affiliations
Bibliographic Information
Book Title: Image Quality Assessment of Computer-generated Images
Book Subtitle: Based on Machine Learning and Soft Computing
Authors: André Bigand, Julien Dehos, Christophe Renaud, Joseph Constantin
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-319-73543-6
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s) 2018
Softcover ISBN: 978-3-319-73542-9Published: 19 March 2018
eBook ISBN: 978-3-319-73543-6Published: 09 March 2018
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XIV, 88
Number of Illustrations: 7 b/w illustrations, 38 illustrations in colour
Topics: Computer Imaging, Vision, Pattern Recognition and Graphics, Computational Intelligence