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Visual Quality Assessment by Machine Learning: SpringerBriefs in Electrical and Computer Engineering

Autor Long Xu, Weisi Lin, C.-C. Jay Kuo
en Limba Engleză Paperback – 27 mai 2015
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.
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Specificații

ISBN-13: 9789812874672
ISBN-10: 9812874674
Pagini: 100
Ilustrații: XIV, 132 p. 19 illus., 16 illus. in color.
Dimensiuni: 155 x 235 x 13 mm
Greutate: 0.22 kg
Ediția:2015
Editura: Springer Nature Singapore
Colecția Springer
Seriile SpringerBriefs in Electrical and Computer Engineering, SpringerBriefs in Signal Processing

Locul publicării:Singapore, Singapore

Public țintă

Research

Cuprins

Introduction.- Fundamental knowledges of machine learning.- Image features and feature processing.- Feature pooling by learning.- Metrics fusion.- Summary and remarks for future research.

Caracteristici

Presents the emerging techniques of learning based visual quality assessment Highlights machine learning techniques and their applications in visual quality assessment Includes a number of real-world examples that readers can implement in their own work Includes supplementary material: sn.pub/extras