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LEADER 00000cam 2200589Ii 4500
006 m o d
007 cr cnu---unuuu
008 180313s2018 sz ob 000 0 eng d
020 9783319735436|q(electronic bk.)
020 3319735438|q(electronic bk.)
024 7 10.1007/978-3-319-73543-6|2doi
050 4 T385
072 7 COM|x000000|2bisacsh
072 7 UYQV|2bicssc
082 04 006.6|223
100 1 Bigand, Andre,|eauthor.
245 10 Image quality assessment of computer-generated images :
|bbased on machine learning and soft computing /|cAndre
Bigand, Julien Dehos, Christophe Renaud, Joseph
264 1 Cham, Switzerland :|bSpringer,|c2018.
300 1 online resource.
338 online resource|bcr|2rdacarrier
347 text file|2rdaft|0http://rdaregistry.info/termList/
490 1 SpringerBriefs in computer science
504 Includes bibliographical references.
505 0 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 --
520 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.
588 0 Online resource; title from PDF title page (EBSCO, viewed
March 14, 2018).
650 0 Computer graphics.|0http://id.loc.gov/authorities/subjects
650 0 Machine learning.|0http://id.loc.gov/authorities/subjects/
650 0 Soft computing.|0http://id.loc.gov/authorities/subjects/
655 4 Electronic books.
700 1 Dehos, Julien,|eauthor.
700 1 Renaud, Christophe,|eauthor.
700 1 Constantin, Joseph,|d1710?-|0http://id.loc.gov/authorities
776 08 |iPrint version:|aBigand, Andre.|tImage quality assessment
of computer-generated images.|dCham, Switzerland :
830 0 SpringerBriefs in computer science.|0http://id.loc.gov/
990 SpringerLink|bSpringer English/International eBooks 2018 -
Full Set|c2018-10-31|yNew collection