Visual Quality Assessment by Machine Learning
(Sprache: Englisch)
The book encompasses the state-of-the-art visual quality assessment (VQA) and learning based visual quality assessment (LB-VQA) by providing a comprehensive overview of the existing relevant methods. It delivers the readers the basic knowledge, systematic...
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Klappentext zu „Visual Quality Assessment by Machine Learning “
The book encompasses the state-of-the-art visual quality assessment (VQA) and learning based visual quality assessment (LB-VQA) by providing a comprehensive overview of the existing relevant methods. It delivers the readers the basic knowledge, systematic overview and new development of VQA. It also encompasses the preliminary knowledge of Machine Learning (ML) to VQA tasks and newly developed ML techniques for the purpose. Hence, firstly, it is particularly helpful to the beginner-readers (including research students) to enter into VQA field in general and LB-VQA one in particular. Secondly, new development in VQA and LB-VQA particularly are detailed in this book, which will give peer researchers and engineers new insights in VQA.
Inhaltsverzeichnis zu „Visual Quality Assessment by Machine Learning “
- Introduction- Feature Extraction
- Learning for Feature Pooling
- Future Metrics
- Summary and Remarks for Future Work
Bibliographische Angaben
- Autoren: Long Xu , Weisi Lin , C.-C. Jay Kuo
- 2015, 2015, XIV, 132 Seiten, 16 farbige Abbildungen, Maße: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 9812874674
- ISBN-13: 9789812874672
- Erscheinungsdatum: 27.05.2015
Sprache:
Englisch
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