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Image Quality Assessment of Computer-generated Images

Based on Machine Learning and Soft Computing

  • André Bigand
  • Julien Dehos
  • Christophe Renaud
  • Joseph Constantin

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. André Bigand, Julien Dehos, Christophe Renaud, Joseph Constantin
    Pages 1-5
  3. André Bigand, Julien Dehos, Christophe Renaud, Joseph Constantin
    Pages 7-17
  4. André Bigand, Julien Dehos, Christophe Renaud, Joseph Constantin
    Pages 19-27
  5. André Bigand, Julien Dehos, Christophe Renaud, Joseph Constantin
    Pages 29-47
  6. André Bigand, Julien Dehos, Christophe Renaud, Joseph Constantin
    Pages 49-70
  7. André Bigand, Julien Dehos, Christophe Renaud, Joseph Constantin
    Pages 71-85
  8. André Bigand, Julien Dehos, Christophe Renaud, Joseph Constantin
    Pages 87-88

About this book

Introduction

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.

Keywords

Computer-generated images Image Quality Metrics Machine Learning Fuzzy Sets Image Quality Assessment Visualization

Authors and affiliations

  • André Bigand
    • 1
  • Julien Dehos
    • 2
  • Christophe Renaud
    • 3
  • Joseph Constantin
    • 4
  1. 1.LISICUniversity of the Littoral Opal Coast LISICCalais CedexFrance
  2. 2.University of the Littoral Opal Coast DunkirkFrance
  3. 3.University of the Littoral Opal Coast DunkirkFrance
  4. 4.Faculty of Science IILebanese University Faculty of Science IIBeirutLebanon

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-73543-6
  • Copyright Information The Author(s) 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-73542-9
  • Online ISBN 978-3-319-73543-6
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • Buy this book on publisher's site