JOURNAL ARTICLE

Center-Emphasized Visual Saliency and a Contrast-Based Full Reference Image Quality Index

Md. Abu LayekA. F. M. Shahab UddinTuyen P. LeTaeChoong ChungEui‐Nam Huh

Year: 2019 Journal:   Symmetry Vol: 11 (3)Pages: 296-296   Publisher: Multidisciplinary Digital Publishing Institute

Abstract

Objective image quality assessment (IQA) is imperative in the current multimedia-intensive world, in order to assess the visual quality of an image at close to a human level of ability. Many parameters such as color intensity, structure, sharpness, contrast, presence of an object, etc., draw human attention to an image. Psychological vision research suggests that human vision is biased to the center area of an image and display screen. As a result, if the center part contains any visually salient information, it draws human attention even more and any distortion in that part will be better perceived than other parts. To the best of our knowledge, previous IQA methods have not considered this fact. In this paper, we propose a full reference image quality assessment (FR-IQA) approach using visual saliency and contrast; however, we give extra attention to the center by increasing the sensitivity of the similarity maps in that region. We evaluated our method on three large-scale popular benchmark databases used by most of the current IQA researchers (TID2008, CSIQ and LIVE), having a total of 3345 distorted images with 28 different kinds of distortions. Our method is compared with 13 state-of-the-art approaches. This comparison reveals the stronger correlation of our method with human-evaluated values. The prediction-of-quality score is consistent for distortion specific as well as distortion independent cases. Moreover, faster processing makes it applicable to any real-time application.

Keywords:
Contrast (vision) Distortion (music) Artificial intelligence Computer science Image quality Human visual system model Computer vision Similarity (geometry) Benchmark (surveying) Image (mathematics) Quality (philosophy) Salient Image processing Pattern recognition (psychology)

Metrics

14
Cited By
1.07
FWCI (Field Weighted Citation Impact)
44
Refs
0.80
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Image and Video Quality Assessment
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Visual Attention and Saliency Detection
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image Fusion Techniques
Physical Sciences →  Engineering →  Media Technology

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