00981nam a2200289 i 450099100346925970753620021217151402.0961025s1983 us a b 001 0 eng d0125073607b11815000-39ule_instLE00301230ExLDip.to Biologiaeng571.622Morrow, John531290Eukaryotic cell genetics /John MorrowNew York :Academic Press,1983xii, 260 p. :ill. ;24 cmCell biologyIncludes Bibliography: p. 225-255 and indexCytogeneticsEukaryotic cells.b1181500027-04-1718-12-02991003469259707536LE003 571.6 MOR01.01 (1983)12003000033366le003-E0.00-l- 00000.i1206445218-12-02Eukaryotic cell genetics899006UNISALENTOle00301-01-96ma -engus 0103981nam 22006375 450 991061926760332120251009102903.09789811933479981193347210.1007/978-981-19-3347-9(MiAaPQ)EBC7119895(Au-PeEL)EBL7119895(CKB)25179632200041(PPN)265856558(DE-He213)978-981-19-3347-9(OCoLC)1348634355(EXLCZ)992517963220004120221019d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierQuality Assessment of Visual Content /by Ke Gu, Hongyan Liu, Chengxu Zhou1st ed. 2022.Singapore :Springer Nature Singapore :Imprint: Springer,2022.1 online resource (256 pages)Advances in Computer Vision and Pattern Recognition,2191-6594Print version: Gu, Ke Quality Assessment of Visual Content Singapore : Springer,c2022 9789811933462 Includes bibliographical references.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.Advances in Computer Vision and Pattern Recognition,2191-6594Image processingImage processingDigital techniquesComputer visionImage ProcessingComputer Imaging, Vision, Pattern Recognition and GraphicsComputer VisionImage processing.Image processingDigital techniques.Computer vision.Image Processing.Computer Imaging, Vision, Pattern Recognition and Graphics.Computer Vision.006.6869Gu Ke1262570Liu HongyanZhou ChengxuMiAaPQMiAaPQMiAaPQBOOK9910619267603321Quality assessment of visual content3041738UNINA