International Journal of Electrical and Computer Engineering (IJECE) Vol. 11, No. 3, June 2021, pp. 2595~2603 ISSN: 2088-8708, DOI: 10.11591/ijece.v11i3.pp2595-2603  2595 Journal homepage: http://ijece.iaescore.com Contrast-distorted image quality assessment based on curvelet domain features Ismail Taha Ahmed 1 , Chen Soong Der 2 , Baraa Tareq Hammad 3 , Norziana Jamil 4 1,3 College of Computer Sciences and Information Technology, University of Anbar, Anbar, Iraq 2 College of Graduate Studies, Universiti Tenaga Nasional, Malaysia 4 College of Computing and Informatics, Universiti Tenaga Nasional, Malaysia Article Info ABSTRACT Article history: Received Sep 11, 2019 Revised Jul 26, 2020 Accepted Sep 24, 2020 Contrast is one of the most popular forms of distortion. Recently, the existing image quality assessment algorithms (IQAs) works focusing on distorted images by compression, noise and blurring. Reduced-reference image quality metric for contrast-changed images (RIQMC) and no reference-image quality assessment (NR-IQA) for contrast-distorted images (NR-IQA-CDI) have been created for CDI. NR-IQA-CDI showed poor performance in two out of three image databases, where the Pearson correlation coefficient (PLCC) were only 0.5739 and 0.7623 in TID2013 and CSIQ database, respectively. Spatial domain features are the basis of NR-IQA-CDI architecture. Therefore, in this paper, the spatial domain features are complementary with curvelet domain features, in order to take advantage of the potent properties of the curvelet in extracting information from images such as multiscale and multidirectional. The experimental outcome rely on K-fold cross validation (K ranged 2-10) and statistical test showed that the performance of NR-IQA- CDI rely on curvelet domain features (NR-IQA-CDI-CvT) significantly surpasses those which are rely on five spatial domain features. Keywords: Contrast-distorted image IQAs NR-IQA NR-IQA-CDI NR-IQA-CDI-CvT This is an open access article under the CC BY-SA license. Corresponding Author: Ismail Taha Ahmed College of Computer Sciences and Information Technology University of Anbar Anbar, Iraq Email: ismail.taha@uoanbar.edu.iq 1. INTRODUCTION Various kinds of distortion such as noise, blurring, fast fading, blocking artifacts and contrast which may appear because of some of certain processes on the image can degrade the quality of images. Subjective and objective methods are two types of IQA or video quality assessment (VQA) used to evaluate the image quality [1, 2]. In real-time applications is impractical to use subjective quality assessment because it takes time and is expensive. Therefore, objective IQA algorithms are the best solution since the role of human is limited in order to predict the quality of image. Objective IQAs can be grouped into full reference (FR), reduce reference (RR) and no reference (NR). These groups require the availability of original image [3-5]. Most of FR-IQA and RR-IQA applications are constrained due to available of original image. Therefore, NR-IQA is the best solution for this case. Recently, the existing NR-IQA works focusing on distorted images by compression, noise and blurring [6, 7]. Contrast is one of the most popular forms of distortion. Figure 1 shows the contrast-distorted image is low gray scale image [8]. poor lighting condition and low quality image acquisition device can produce contrast distortion [9, 10]. Unfortunately, the current work carried out in the area of NR-IQA for CDI is very minimal.