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LEADER 00000cam  2200589Ii 4500 
001    1028553140 
003    OCoLC 
005    20181101051514.2 
006    m     o  d         
007    cr cnu---unuuu 
008    180313s2018    sz      ob    000 0 eng d 
019    1028840781|a1028881876|a1028953631|a1028968860|a1029012701
020    9783319735436|q(electronic bk.) 
020    3319735438|q(electronic bk.) 
020    |z9783319735429|q(print) 
020    |z331973542X 
024 7  10.1007/978-3-319-73543-6|2doi 
035    (OCoLC)1028553140|z(OCoLC)1028840781|z(OCoLC)1028881876
040    N$T|beng|erda|epn|cN$T|dGW5XE|dN$T|dYDX|dAZU|dOCLCF|dEBLCP
049    MAIN 
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. 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
340    |gpolychrome|2rdacc|0
347    text file|2rdaft|0
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.|0
650  0 Machine learning.|0
650  0 Soft computing.|0
655  4 Electronic books. 
700 1  Dehos, Julien,|eauthor. 
700 1  Renaud, Christophe,|eauthor. 
700 1  Constantin, Joseph,|d1710?-|0
776 08 |iPrint version:|aBigand, Andre.|tImage quality assessment
       of computer-generated images.|dCham, Switzerland : 
       Springer, 2018|z331973542X|z9783319735429
830  0 SpringerBriefs in computer science.|0
990    SpringerLink|bSpringer English/International eBooks 2018 -
       Full Set|c2018-10-31|yNew collection 
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