Image quality assessment of computer-generated images : based on machine learning and soft computing / André Bigand, Julien Dehos, Christophe Renaud, Joseph Constantin.
2018
T385
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Title
Image quality assessment of computer-generated images : based on machine learning and soft computing / André Bigand, Julien Dehos, Christophe Renaud, Joseph Constantin.
Author
ISBN
9783319735436 (electronic book)
3319735438 (electronic book)
9783319735429
331973542X
3319735438 (electronic book)
9783319735429
331973542X
Published
Cham, Switzerland : Springer, 2018.
Language
English
Description
1 online resource.
Item Number
10.1007/978-3-319-73543-6 doi
Call Number
T385
Dewey Decimal Classification
006.6
Summary
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-valued fuzzy 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.
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Online resource; title from PDF title page (viewed March 14, 2018)
Series
SpringerBriefs in computer science.
Available in Other Form
Print version: 9783319735429
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Table of Contents
Introduction
Monte-Carlo Methods for Image Synthesis
Visual Impact of Rendering on Image Quality
Full-reference Methods and Machine Learning
No-reference Methods and Fuzzy Sets
Reduced-reference Methods
Conclusion.
Monte-Carlo Methods for Image Synthesis
Visual Impact of Rendering on Image Quality
Full-reference Methods and Machine Learning
No-reference Methods and Fuzzy Sets
Reduced-reference Methods
Conclusion.