| Título: | Machine Learning to Design Full-reference Image Quality Assessment Algorithm |
| Autores: |
Yu Ling, Wang; North University of China Hu, Yang; North University of China |
| Fecha: | 2013-06-01 |
| Publicador: | TELKOMNIKA: Indonesian journal of electrical engineering |
| Fuente: |
 |
| Tipo: |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| Tema: | FR-IQA algorithm; classification; SVM; SVM regression |
| Descripción: | A crucial step in image compression is the evaluation of its performance, and more precisely, available ways to measure the quality of compressed images. In this paper, based on a learned classification process in order to respect human observers， a method namely Machine Learning-based Image Quality Measure (MLIQM) is proposed，which classifies the quality using multi-Support Vector Machine (SVM) classification according to the quality scale recommended by the ITU. Then, the classification process is performed to provide the final quality class of the considered image. Finally, once a quality class is associated to the considered image, a specific SVM regression is performed to score its quality. Obtained results are compared with the one obtained applying classical Full-Reference Image Quality Assessment (FR-IQA) algorithms to judge the efficiency of the proposed method. |
| Idioma: | Inglés |