000			03164nam a22003015i 4500
008			221019s2022 si | s |||| 0|eng d
020			_a9789811933479 _9978-981-19-3347-9
082	0	4	_a621.382 _223
100	1		_aGu, Ke. _eauthor. _0(orcid)0000-0001-5540-3235 _1https://orcid.org/0000-0001-5540-3235 _91467974
245	1	0	_aQuality Assessment of Visual Content _h[electronic resource] / _cby Ke Gu, Hongyan Liu, Chengxu Zhou.
250			_a1st ed. 2022.
264		1	_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2022.
300			_aXVII, 242 p. 75 illus., 66 illus. in color. _bonline resource.
490	1		_aAdvances in Computer Vision and Pattern Recognition, _x2191-6594
505	0		_aChapter 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			_aThis 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	_aImage processing.
650		0	_aImage processing-Digital techniques.
650		0	_aComputer vision.
650	1	4	_aImage Processing.
650	2	4	_aComputer Imaging, Vision, Pattern Recognition and Graphics.
650	2	4	_aComputer Vision.
700	1		_aLiu, Hongyan. _eauthor. _91467975
700	1		_aZhou, Chengxu. _eauthor. _91467976
830		0	_aAdvances in Computer Vision and Pattern Recognition, _x2191-6594 _91214047
856	4	0	_uhttps://doi.org/10.1007/978-981-19-3347-9 _3Click Here
887			_aAkhil Chandra Saren
999			_c1580951 _d1580951