Visual quality assessment by machine learning

This document was uploaded by one of our users. The uploader already confirmed that they had the permission to publish it. If you are author/publisher or own the copyright of this documents, please report to us by using this DMCA report form.

Simply click on the Download Book button.

Yes, Book downloads on Ebookily are 100% Free.

Sometimes the book is free on Amazon As well, so go ahead and hit "Search on Amazon"

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  Read more...

Abstract: 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

Author(s): Kuo, C.-C. Jay; Lin, Weisi; Xu, Long
Series: SpringerBriefs in electrical and computer engineering
Publisher: Springer Singapore : Imprint: Springer
Year: 2015

Language: English
Pages: 132
Tags: Engineering.;Image processing.;Computational intelligence.;Signal, Image and Speech Processing.;Image Processing and Computer Vision.;Computational Intelligence.

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