A new no-reference image quality assessment based on SVR fusion

Author

Eddine, Dakkar Borhen ; Fella, Hachouf ; Seghir, Zianou Ahmed

Author_Institution

Lab. d´Autom. et de Robot., Univ. Constantine 1, Constantine, Algeria

fYear

2014

fDate

10-12 Dec. 2014

Firstpage

1

Lastpage

6

Abstract

This paper presents a new concept of assessing image quality. It is based on support vector regression (SVR) fusion. Despite the variety of the proposed IQM measures, no efficient and sufficient measure gives good performance over different distortions. Motivated by this problem, a new measure for No reference Image Quality Assessment Based on SVR Fusion (NR BSVRF) is constituted. First, five recent no reference measures are selected to form a quality vector of an image, then the quality vector is fused via SVR. The SVR is trained to have a model that is used to predict the image quality. Obtained results are promising. They have shown better performance compared to existing No-reference image quality measures.

Keywords

image fusion; regression analysis; support vector machines; vectors; IQM measures; NR BSVRF; SVR fusion; no-reference image quality assessment; quality vector; support vector regression fusion; Distortion measurement; Image quality; Support vector machines; Transform coding; Vectors; Visualization; Image Quality Assessment(IQA); fusion; support vector regression (SVR);

fLanguage

English

Publisher

ieee

Conference_Titel

Visual Information Processing (EUVIP), 2014 5th European Workshop on

Conference_Location

Paris

Type

conf

DOI

10.1109/EUVIP.2014.7018390

Filename

7018390