Paper Info
Title
Eye Movement-Based Analysis On Methodologies And Efficiency In The Process Of Image Noise Evaluation
Abstract
Noise level (image quality) evaluation is an important and popular topic in many applications. However, the knowledge of how people visually explore distorted images for making decision on noise evaluation is rather limited. In this paper, we conducted psychophysical eye-tracking studies to deeply understand the process of image noise evaluation. We identified two different types of methodologies in the evaluation processing, speed-driven and accuracy-driven respectively, in terms of both evaluation time and decision error. The speed-driven methodology, compared with the accuracy-driven one, uses less time to give evaluation results, with shorter fixation duration and stronger central bias. Furthermore, based on the utilization of temporal-spatial entropy analysis on eye movement data, a quantitative measure is obtained to show significant correlation with the decision-making efficiency of evaluation processing, which is characterized by evaluation time and decision error. As a result, the new measure may be used as a proxy definition for this decision-making efficiency.
Year
DOI
Venue
2019
10.1007/978-3-030-30508-6_3
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: IMAGE PROCESSING, PT III
Keywords
DocType
Volume
Noise level evaluation, Temporal-spatial entropy analysis, Reduced redundant information, Decision-making efficiency
Conference
11729
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Cheng Peng100.34
Qing Xu2103.49
Yuejun Guo3196.17
Klaus Schoeffmann450963.01