國立高雄大學圖資館 |

Language: English

Back

Quality assessment of visual content

Record Type:	Electronic resources : Monograph/item
Title/Author:	Quality assessment of visual contentby Ke Gu, Hongyan Liu, Chengxu Zhou.
Author:	Gu, Ke.
other author:	Liu, Hongyan.
Published:	Singapore :Springer Nature Singapore :2022.
Description:	xvii, 242 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Computer graphicsQuality control.
Online resource:	https://doi.org/10.1007/978-981-19-3347-9
ISBN:	9789811933479$q(electronic bk.)

Quality assessment of visual content
Gu, Ke.

Quality assessment of visual content[electronic resource] /by Ke Gu, Hongyan Liu, Chengxu Zhou. - Singapore :Springer Nature Singapore :2022. - xvii, 242 p. :ill. (chiefly color), digital ;24 cm. - Advances in computer vision and pattern recognition,2191-6594. - Advances in computer vision and pattern recognition..

Chapter 1. Introduction -- Chapter 2. Quality Assessment of Screen Content Images -- Chapter 3. Quality Assessment of 3D-Synthesized Images -- Chapter 4. Quality Assessment of Sonar Images -- Chapter 5. Quality Assessment of Enhanced Images -- Chapter 6. Quality Assessment of Light-Field Image -- Chapter 7. Quality Assessment of Virtual Reality Images -- Chapter 8. Quality Assessment of Super-Resolution Images.

This book provides readers with a comprehensive review of image quality assessment technology, particularly applications on screen content images, 3D-synthesized images, sonar images, enhanced images, light-field images, VR images, and super-resolution images. It covers topics containing structural variation analysis, sparse reference information, multiscale natural scene statistical analysis, task and visual perception, contour degradation measurement, spatial angular measurement, local and global assessment metrics, and more. All of the image quality assessment algorithms of this book have a high efficiency with better performance compared to other image quality assessment algorithms, and the performance of these approaches mentioned above can be demonstrated by the results of experiments on real-world images. On the basis of this, those interested in relevant fields can use the results obtained through these quality assessment algorithms for further image processing. The goal of this book is to facilitate the use of these image quality assessment algorithms by engineers and scientists from various disciplines, such as optics, electronics, math, photography techniques and computation techniques. The book can serve as a reference for graduate students who are interested in image quality assessment techniques, for front-line researchers practicing these methods, and for domain experts working in this area or conducting related application development.

ISBN: 9789811933479$q(electronic bk.)

Standard No.: 10.1007/978-981-19-3347-9doiSubjects--Topical Terms:

936806
Computer graphics
--Quality control.

LC Class. No.: T385 / .G8 2022

Dewey Class. No.: 006.6

Quality assessment of visual content
LDR:03011nmm a2200361 a 4500 001 631161
003 DE-He213
005 20221019040309.0
006 m d
007 cr nn 008maaau
008 230411s2022 si s 0 eng d
020 $a 9789811933479$q(electronic bk.)
020 $a 9789811933462$q(paper)
024 7 $a 10.1007/978-981-19-3347-9 $2 doi
035 $a 978-981-19-3347-9
040 $a GP $c GP
041 0 $a eng
050 4 $a T385 $b .G8 2022
072 7 $a TJF $2 bicssc
072 7 $a UYT $2 bicssc
072 7 $a COM016000 $2 bisacsh
072 7 $a TJF $2 thema
072 7 $a UYT $2 thema
082 0 4 $a 006.6 $2 23
090 $a T385 $b .G896 2022
100 1 $a Gu, Ke. $3 936804
245 1 0 $a Quality assessment of visual content $h [electronic resource] / $c by Ke Gu, Hongyan Liu, Chengxu Zhou.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2022.
300 $a xvii, 242 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Advances in computer vision and pattern recognition, $x 2191-6594
505 0 $a Chapter 1. Introduction -- Chapter 2. Quality Assessment of Screen Content Images -- Chapter 3. Quality Assessment of 3D-Synthesized Images -- Chapter 4. Quality Assessment of Sonar Images -- Chapter 5. Quality Assessment of Enhanced Images -- Chapter 6. Quality Assessment of Light-Field Image -- Chapter 7. Quality Assessment of Virtual Reality Images -- Chapter 8. Quality Assessment of Super-Resolution Images.
520 $a This book provides readers with a comprehensive review of image quality assessment technology, particularly applications on screen content images, 3D-synthesized images, sonar images, enhanced images, light-field images, VR images, and super-resolution images. It covers topics containing structural variation analysis, sparse reference information, multiscale natural scene statistical analysis, task and visual perception, contour degradation measurement, spatial angular measurement, local and global assessment metrics, and more. All of the image quality assessment algorithms of this book have a high efficiency with better performance compared to other image quality assessment algorithms, and the performance of these approaches mentioned above can be demonstrated by the results of experiments on real-world images. On the basis of this, those interested in relevant fields can use the results obtained through these quality assessment algorithms for further image processing. The goal of this book is to facilitate the use of these image quality assessment algorithms by engineers and scientists from various disciplines, such as optics, electronics, math, photography techniques and computation techniques. The book can serve as a reference for graduate students who are interested in image quality assessment techniques, for front-line researchers practicing these methods, and for domain experts working in this area or conducting related application development.
650 0 $a Computer graphics $x Quality control. $3 936806
650 1 4 $a Image Processing. $3 273705
650 2 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 274492
650 2 4 $a Computer Vision. $3 913138
700 1 $a Liu, Hongyan. $3 485811
700 1 $a Zhou, Chengxu. $3 936805
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Advances in computer vision and pattern recognition. $3 559645
856 4 0 $u https://doi.org/10.1007/978-981-19-3347-9
950 $a Computer Science (SpringerNature-11645)