|
|
|
|
| LEADER |
03026nmm a2200433 u 4500 |
| 001 |
EB001903681 |
| 003 |
EBX01000000000000001066587 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
201103 ||| eng |
| 020 |
|
|
|a 9789811577642
|
| 100 |
1 |
|
|a Ding, Yong
|
| 245 |
0 |
0 |
|a Stereoscopic Image Quality Assessment
|h Elektronische Ressource
|c by Yong Ding, Guangming Sun
|
| 250 |
|
|
|a 1st ed. 2020
|
| 260 |
|
|
|a Singapore
|b Springer Nature Singapore
|c 2020, 2020
|
| 300 |
|
|
|a IX, 169 p. 55 illus., 18 illus. in color
|b online resource
|
| 505 |
0 |
|
|a Introduction -- Basic of 2D Image Quality Assessment -- The Difference Between 2D IQA and 3D IQA -- Stereoscopic Image Quality Assessment Based on 2D IQA Models -- Stereoscopic Image Quality Assessment Based on Binocular Vision -- Learning Perceptual Quality of Stereopsis from Human Visual Properties -- Stereoscopic Image Quality Assessment Based on Deep Convolutional Neural Models -- Summary and Future Directions
|
| 653 |
|
|
|a Measurement
|
| 653 |
|
|
|a Electronics and Microelectronics, Instrumentation
|
| 653 |
|
|
|a Image processing / Digital techniques
|
| 653 |
|
|
|a Computer vision
|
| 653 |
|
|
|a Database Management System
|
| 653 |
|
|
|a Artificial Intelligence
|
| 653 |
|
|
|a Image processing
|
| 653 |
|
|
|a Computer Imaging, Vision, Pattern Recognition and Graphics
|
| 653 |
|
|
|a Image Processing
|
| 653 |
|
|
|a Artificial intelligence
|
| 653 |
|
|
|a Electronics
|
| 653 |
|
|
|a Measuring instruments
|
| 653 |
|
|
|a Database management
|
| 653 |
|
|
|a Measurement Science and Instrumentation
|
| 700 |
1 |
|
|a Sun, Guangming
|e [author]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b Springer
|a Springer eBooks 2005-
|
| 490 |
0 |
|
|a Advanced Topics in Science and Technology in China
|
| 028 |
5 |
0 |
|a 10.1007/978-981-15-7764-2
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-981-15-7764-2?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 621.382
|
| 520 |
|
|
|a This book provides a comprehensive review of all aspects relating to visual quality assessment for stereoscopic images, including statistical mathematics, stereo vision and deep learning. It covers the fundamentals of stereoscopic image quality assessment (SIQA), the relevant engineering problems and research significance, and also offers an overview of the significant advances in visual quality assessment for stereoscopic images, discussing and analyzing the current state-of-the-art in SIQA algorithms, the latest challenges and research directions as well as novel models and paradigms. In addition, a large number of vivid figures and formulas help readers gain a deeper understanding of the foundation and new applications of objective stereoscopic image quality assessment technologies. Reviewing the latest advances, challenges and trends in stereoscopic image quality assessment, this book is a valuable resource for researchers, engineers andgraduate students working in related fields, including imaging, displaying and image processing, especially those interested in SIQA research
|