LEADER 03981nam 22006375 450 001 9910619267603321 005 20251009102903.0 010 $a9789811933479 010 $a9811933472 024 7 $a10.1007/978-981-19-3347-9 035 $a(MiAaPQ)EBC7119895 035 $a(Au-PeEL)EBL7119895 035 $a(CKB)25179632200041 035 $a(PPN)265856558 035 $a(DE-He213)978-981-19-3347-9 035 $a(OCoLC)1348634355 035 $a(EXLCZ)9925179632200041 100 $a20221019d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aQuality Assessment of Visual Content /$fby Ke Gu, Hongyan Liu, Chengxu Zhou 205 $a1st ed. 2022. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2022. 215 $a1 online resource (256 pages) 225 1 $aAdvances in Computer Vision and Pattern Recognition,$x2191-6594 311 08$aPrint version: Gu, Ke Quality Assessment of Visual Content Singapore : Springer,c2022 9789811933462 320 $aIncludes bibliographical references. 327 $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. 330 $aThis book provides readers with a comprehensive review of image quality assessment technology, particularly applications on screen content images, 3D-synthesized images, sonar images, enhanced images, light-field images, VR images, and super-resolution images. It covers topics containing structural variation analysis, sparse reference information, multiscale natural scene statistical analysis, task and visual perception, contour degradation measurement, spatial angular measurement, local and global assessment metrics, and more. All of the image quality assessment algorithms of this book have a high efficiency with better performance compared to other image quality assessment algorithms, and the performance of these approaches mentioned above can be demonstrated by the results of experiments on real-world images. On the basis of this, those interested in relevant fields can use the results obtained through these quality assessment algorithms for further image processing. The goal of this book is to facilitate the use of these image quality assessment algorithms by engineers and scientists from various disciplines, such as optics, electronics, math, photography techniques and computation techniques. The book can serve as a reference for graduate students who are interested in image quality assessment techniques, for front-line researchers practicing these methods, and for domain experts working in this area or conducting related application development. 410 0$aAdvances in Computer Vision and Pattern Recognition,$x2191-6594 606 $aImage processing 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aImage Processing 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aComputer Vision 615 0$aImage processing. 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 14$aImage Processing. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aComputer Vision. 676 $a006.6869 700 $aGu$b Ke$01262570 702 $aLiu$b Hongyan 702 $aZhou$b Chengxu 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910619267603321 996 $aQuality assessment of visual content$93041738 997 $aUNINA