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100 1 _aBigand, André.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aImage Quality Assessment of Computer-generated Images
_h[electronic resource] :
_bBased on Machine Learning and Soft Computing /
_cby André Bigand, Julien Dehos, Christophe Renaud, Joseph Constantin.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIV, 88 p. 45 illus., 38 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5768
500 _aAcceso multiusuario
505 0 _aIntroduction -- 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.
520 _aImage 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.
541 _fUABC ;
_cTemporal ;
_d01/01/2021-12/31/2023.
650 0 _aOptical data processing.
650 0 _aComputational intelligence.
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I22005
650 2 4 _aComputational Intelligence.
_0https://scigraph.springernature.com/ontologies/product-market-codes/T11014
700 1 _aDehos, Julien.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aRenaud, Christophe.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aConstantin, Joseph.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319735429
776 0 8 _iPrinted edition:
_z9783319735443
830 0 _aSpringerBriefs in Computer Science,
_x2191-5768
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-319-73543-6
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
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