Abstract
No Reference Image Quality Assessment (NR-IQA) refers to algorithms that predict the quality of distorted image where the reference image is not available. NR-IQA algorithms are divided into two categories: specific distortion quality assessment and general purpose quality assessment. The first type of algorithms deals with a specific distortion and predict the quality of the image based on this distortion. Which means it assumes that the distortion in the image is known. On the other hand, the general purpose quality assessment type predicts the quality of image with no information about the distortion that affecting the tested image. A two-stage framework, is proposed by Moorthy and Bovik [ 1], which classifies the distortion followed by distortion-specific quality assessment method. Our proposal here is to improve the classification portion by investigating the performance of different classification techniques and different features. Each feature is validated using different features evaluation techniques. As a result, we construct a set of optimal features that classify image distortions with a high accuracy rate.