| 书目名称 | Image Quality Assessment of Computer-generated Images | | 副标题 | Based on Machine Lea | | 编辑 | André Bigand,Julien Dehos,Joseph Constantin | | 视频video | | | 概述 | Enriches understanding of Image Quality Assessment.Explains how computer-generated images are rendered and how this introduces visual noise.Demonstrates the use of learning machines and fuzzy-sets as | | 丛书名称 | SpringerBriefs in Computer Science | | 图书封面 |  | | 描述 | .Image Quality Assessment is well-known for measuring the perceived image degradation of natural scene images but is still an emerging topic for computer-generated images. This book addresses this problem and presents recent advances based on soft computing. It is aimed at students, practitioners and researchers in the field of image processing and related areas such as computer graphics and visualization..In this book, we first clarify the differences between natural scene images and computer-generated images, and address the problem of Image Quality Assessment (IQA) by focusing on the visual perception of noise. Rather than using known perceptual models, we first investigate the use of soft computing approaches, classically used in Artificial Intelligence, as full-reference and reduced-reference metrics. Thus, by creating Learning Machines, such as SVMs and RVMs, we can assess the perceptual quality of a computer-generated image. We also investigate the use of interval-valuedfuzzy sets as a no-reference metric...These approaches are treated both theoretically and practically, for the complete process of IQA. The learning step is performed using a database built from experiments w | | 出版日期 | Book 2018 | | 关键词 | Computer-generated images; Image Quality Metrics; Machine Learning; Fuzzy Sets; Image Quality Assessment | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-73543-6 | | isbn_softcover | 978-3-319-73542-9 | | isbn_ebook | 978-3-319-73543-6Series ISSN 2191-5768 Series E-ISSN 2191-5776 | | issn_series | 2191-5768 | | copyright | The Author(s) 2018 |
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发表于 2025-3-21 18:30:43
