國立虎尾科技大學 |

Image Quality Assessment of Computer-generated Images = Based on Machine Learning and Soft Computing /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Image Quality Assessment of Computer-generated Images/ by André Bigand, Julien Dehos, Christophe Renaud, Joseph Constantin.
其他題名:	Based on Machine Learning and Soft Computing /
作者:	Bigand, André.
其他作者:	Dehos, Julien.
面頁冊數:	XIV, 88 p. 45 illus., 38 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Optical data processing. -
電子資源:	https://doi.org/10.1007/978-3-319-73543-6
ISBN:	9783319735436

Image Quality Assessment of Computer-generated Images = Based on Machine Learning and Soft Computing /
Bigand, André.

Image Quality Assessment of Computer-generated ImagesBased on Machine Learning and Soft Computing /[electronic resource] :by André Bigand, Julien Dehos, Christophe Renaud, Joseph Constantin. - 1st ed. 2018. - XIV, 88 p. 45 illus., 38 illus. in color.online resource. - SpringerBriefs in Computer Science,2191-5768. - SpringerBriefs in Computer Science,.

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.

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.

ISBN: 9783319735436

Standard No.: 10.1007/978-3-319-73543-6doiSubjects--Topical Terms:

639187
Optical data processing.

LC Class. No.: TA1630-1650

Dewey Class. No.: 006.6

Image Quality Assessment of Computer-generated Images = Based on Machine Learning and Soft Computing /
LDR:03303nam a22003975i 4500 001 994759
003 DE-He213
005 20200703152250.0
007 cr nn 008mamaa
008 201225s2018 gw | s |||| 0|eng d
020 $a 9783319735436 $9 978-3-319-73543-6
024 7 $a 10.1007/978-3-319-73543-6 $2 doi
035 $a 978-3-319-73543-6
050 4 $a TA1630-1650
072 7 $a UYQV $2 bicssc
072 7 $a COM016000 $2 bisacsh
072 7 $a UYQV $2 thema
082 0 4 $a 006.6 $2 23
100 1 $a Bigand, André. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1285941
245 1 0 $a Image Quality Assessment of Computer-generated Images $h [electronic resource] : $b Based on Machine Learning and Soft Computing / $c by André Bigand, Julien Dehos, Christophe Renaud, Joseph Constantin.
250 $a 1st ed. 2018.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a XIV, 88 p. 45 illus., 38 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a SpringerBriefs in Computer Science, $x 2191-5768
505 0 $a 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.
520 $a 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.
650 0 $a Optical data processing. $3 639187
650 0 $a Computational intelligence. $3 568984
650 1 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 671334
650 2 4 $a Computational Intelligence. $3 768837
700 1 $a Dehos, Julien. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1285942
700 1 $a Renaud, Christophe. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1285943
700 1 $a Constantin, Joseph. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1285944
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319735429
776 0 8 $i Printed edition: $z 9783319735443
830 0 $a SpringerBriefs in Computer Science, $x 2191-5768 $3 1255334
856 4 0 $u https://doi.org/10.1007/978-3-319-73543-6
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)