Open AccessA generalized quality assessment method for natural and screen content images
Author(s) -
Loh WoeiTan,
Bong David B. L.
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12016
Subject(s) - prewitt operator , computer science , artificial intelligence , image quality , kernel (algebra) , computer vision , pattern recognition (psychology) , quality (philosophy) , content (measure theory) , enhanced data rates for gsm evolution , image (mathematics) , image processing , mathematics , edge detection , mathematical analysis , philosophy , epistemology , combinatorics
Abstract A generalized objective quality assessment method is proposed for natural images and screen content images. Since natural images and screen content images have different statistical properties, the modelling of a generalized quality assessment method that works for both types of images is complicated because some properties of natural images and screen content images are conflicting to one another. The proposed method assesses the perceptual quality of an image based on edge magnitude and direction. In this method, an image is first separated into regions with high and low gradients. Gradient is used due to the small perceptual span of the human visual system for textual content. For high gradient regions, small kernel size of Prewitt operators is used to obtain the gradient magnitude and direction. Correspondingly, bigger kernel size of Prewitt operators is utilized for low gradient regions. Visual quality indices are computed from both regions and pooled to obtain the final quality index. From the performance comparison, it is shown that the proposed method could assess the perceived quality of natural images and screen content images with high accuracy.