Paper Info
Title
No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features
Abstract
With the development of digital imaging techniques, image quality assessment methods are receiving more attention in the literature. Since distortion-free versions of camera images in many practical, everyday applications are not available, the need for effective no-reference image quality assessment algorithms is growing. Therefore, this paper introduces a novel no-reference image quality assessment algorithm for the objective evaluation of authentically distorted images. Specifically, we apply a broad spectrum of local and global feature vectors to characterize the variety of authentic distortions. Among the employed local features, the statistics of popular local feature descriptors, such as SURF, FAST, BRISK, or KAZE, are proposed for NR-IQA; other features are also introduced to boost the performances of local features. The proposed method was compared to 12 other state-of-the-art algorithms on popular and accepted benchmark datasets containing RGB images with authentic distortions (CLIVE, KonIQ-10k, and SPAQ). The introduced algorithm significantly outperforms the state-of-the-art in terms of correlation with human perceptual quality ratings.
Year
DOI
Venue
2022
10.3390/jimaging8060173
JOURNAL OF IMAGING
Keywords
DocType
Volume
no-reference image quality assessment, quality-aware features, image statistics
Journal
8
Issue
ISSN
Citations 
6
2313-433X
0
PageRank 
References 
Authors
0.34
0
1
Name
Order
Citations
PageRank
Domonkos Varga1134.29