Blind image quality assessment via a two-stage non-parametric framework

Manap, Redzuan Abdul, Frangi, Alejandro and Shao, Ling (2015) Blind image quality assessment via a two-stage non-parametric framework. In: 3rd IAPR Asian Conference on Pattern Recognition (ACPR), 3-6 November 2015, Kuala Lumpur.

Full text not available from this repository. (Request a copy)
Official URL:


In this paper, a no-reference image quality assessment (NR-IQA) algorithm based on a two-stage non-parametric framework is presented. At the first stage, the type of distortion affecting the test image patches is first identified via a nearest-neighbor (NN) based classifier. Utilizing the differential mean opinion score (DMOS) values associated with the labelled patches within the identified distortion class, the quality of each test patch is then predicted using k-NN regression. The predicted scores are then pooled together to obtain the quality score of the test image. The proposed algorithm is simple yet effective. No training phase is required and the algorithm also offers prediction of a local region's quality which is not available in most of the previous NR-IQA methods. Experimental results on the standard LIVE IQA database indicate that the proposed algorithm correlates highly with human perceptual measures and deliver competitive performance to state-of-the-art NR-IQA algorithms.

Item Type: Conference or Workshop Item (Paper)
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 05 Aug 2016 08:50
Last Modified: 12 Oct 2019 22:52

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics