A Critical Analysis on Perceptual Contrast and its Use in Visual Information Analysis and Processing

Contrast in visible images is one of the most relevant characteristics of visual signals. Since the pioneering works performed in vision psychology and optics, different definitions have been proposed in the literature. However, for the time being there exist no definition of contrast on which the v...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2020-01, Vol.8, p.1-1
Main Authors: Beghdadi, Azeddine, Qureshi, Muhammad Ali, Amirshahi, Seyed Ali, Chetouani, Aladine, Pedersen, Marius
Format: Article
Language:English
Subjects:
Contrast Enhancement
Contrast Measures
Data analysis
Data processing
Image contrast
Image Enhancement
Image processing
Image Quality
Information management
Information processing
Just Noticeable Difference (JND)
Machine learning
Perceptual Approaches
Perceptual Contrast
Psychology
Vision
Visual signals
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Contrast in visible images is one of the most relevant characteristics of visual signals. Since the pioneering works performed in vision psychology and optics, different definitions have been proposed in the literature. However, for the time being there exist no definition of contrast on which the vision research and visual processing scientific community can agree on. This makes it critical to have a clear view on the notion of contrast and its use in various applications. One issue to consider is how to define and particularly use this important measure in developing image processing and analysis methods. In this paper, we present a critical review of contrast measures and associated models developed by the scientific community in vision, optics, and image processing. We also provide learned lessons and guidelines on the use of appropriate contrast measures in selected visual information processing and analysis applications. We discuss challenges and propose research avenues for models enriched by recent findings in the field of human vision research and machine learning. We believe that this work serves as a guideline and can potentially open new research perspectives to the scientific community working on visual information processing and analysis.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3019350