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Visual Quality Assessment by Machine Learning / / by Long Xu, Weisi Lin, C.-C. Jay Kuo



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Autore: Xu Long Visualizza persona
Titolo: Visual Quality Assessment by Machine Learning / / by Long Xu, Weisi Lin, C.-C. Jay Kuo Visualizza cluster
Pubblicazione: Singapore : , : Springer Singapore : , : Imprint : Springer, , 2015
Edizione: 1st ed. 2015.
Descrizione fisica: 1 online resource (142 p.)
Disciplina: 006.31
Soggetto topico: Signal processing
Image processing
Speech processing systems
Optical data processing
Computational intelligence
Signal, Image and Speech Processing
Image Processing and Computer Vision
Computational Intelligence
Persona (resp. second.): LinWeisi
KuoC.-C. Jay
Note generali: Description based upon print version of record.
Nota di bibliografia: Includes bibliographical references at the end of each chapters.
Nota di contenuto: Introduction -- Fundamental knowledges of machine learning -- Image features and feature processing -- Feature pooling by learning -- Metrics fusion -- Summary and remarks for future research.
Sommario/riassunto: 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.
Titolo autorizzato: Visual Quality Assessment by Machine Learning  Visualizza cluster
ISBN: 981-287-468-2
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910299826503321
Lo trovi qui: Univ. Federico II
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Serie: SpringerBriefs in Signal Processing, . 2196-4076