Name: Image Quality Assessment of Computer-Generated Images: Based on Machine Learning and Soft Computing (Springerbriefs in Computer Science) (en Inglés)
Price: 267.12 PEN
Availability: InStock
Author: André Bigand; Julien Dehos; Christophe Renaud
ISBN: 9783319735429

portada Image Quality Assessment of Computer-Generated Images: Based on Machine Learning and Soft Computing (Springerbriefs in Computer Science) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

André Bigand; Julien Dehos; Christophe Renaud

Año

2018

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

ISBN13

9783319735429

N° edición

Categorías

Reconocimiento de formas
Visión por ordenador

Image Quality Assessment of Computer-Generated Images: Based on Machine Learning and Soft Computing (Springerbriefs in Computer Science) (en Inglés)

André Bigand; Julien Dehos; Christophe Renaud (Autor) · Springer · Tapa Blanda

Libro Nuevo

S/ 267,12

S/ 534,25

Ahorras: S/ 267,12

50% descuento

Envío normal

Origen: Estados Unidos (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Jueves 23 de Mayo y el Jueves 06 de Junio.

Lo recibirás en cualquier lugar de Perú entre 2 y 5 días hábiles luego del envío.

Reseña del libro "Image Quality Assessment of Computer-Generated Images: Based on Machine Learning and Soft Computing (Springerbriefs in Computer Science) (en Inglés)"

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-valued fuzzy 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 with human users and the resulting models can be used for any image computed with a stochastic rendering algorithm. This can be useful for detecting the visual convergence of the different parts of an image during the rendering process, and thus to optimize the computation. These models can also be extended to other applications that handle complex models, in the fields of signal processing and image processing.