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001			394224
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008			221019s2022 si | s |||| 0|eng d
020			_a9789811933479
024	7		_a10.1007/978-981-19-3347-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
100	1		_aGu, Ke _eautor _0(orcid)0000-0001-5540-3235 _1https://orcid.org/0000-0001-5540-3235 _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aQuality Assessment of Visual Content _cby Ke Gu, Hongyan Liu, Chengxu Zhou
250			_a1st edition 2022
264		1	_aSingapore _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XVII, 242 páginas) _b75 ilustraciones, 66 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
700	1		_aLiu, Hongyan _eautor _0(orcid)0000-0002-3990-9639 _1https://orcid.org/0000-0002-3990-9639 _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aZhou, Chengxu _eautor _0(orcid)0000-0002-6348-9910 _1https://orcid.org/0000-0002-6348-9910 _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9789811933462
776	0	8	_iPrinted edition: _z9789811933486
776	0	8	_iPrinted edition: _z9789811933493
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-3347-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSpringer_Computer_2022
999			_c394224 _d394224