114,47 €
127,19 €
-10% with code: EXTRA
Visual Quality Assessment by Machine Learning
Visual Quality Assessment by Machine Learning
114,47
127,19 €
  • We will send in 10–14 business days.
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 (inc…
  • Publisher:
  • Year: 2015
  • Pages: 132
  • ISBN-10: 9812874674
  • ISBN-13: 9789812874672
  • Format: 15.6 x 23.4 x 0.8 cm, softcover
  • Language: English
  • SAVE -10% with code: EXTRA

Visual Quality Assessment by Machine Learning (e-book) (used book) | bookbook.eu

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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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  • Author: Long Xu
  • Publisher:
  • Year: 2015
  • Pages: 132
  • ISBN-10: 9812874674
  • ISBN-13: 9789812874672
  • Format: 15.6 x 23.4 x 0.8 cm, softcover
  • Language: English English

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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