Proceedings of 2011 International Conference on Electronic &Amp; Mechanical Engineering and Information Technology 2011
DOI: 10.1109/emeit.2011.6023384
|View full text |Cite
|
Sign up to set email alerts
|

Structure similarity image quality assessment based on visual perception

Abstract: An image quality objective assessment method of wavelet domain structure similarity (WDSSIM) is proposed by considering human visual features for some deficiencies of spatial structural similarity (SSIM) assessment method while measuring the quality of geometric distorted images and noise polluted images. The method includes three steps: a) decomposing the reference image and the distorted image into sub-band images with different scales and frequencies by wavelet transform, and acquiring human visual in-band … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2013
2013
2023
2023

Publication Types

Select...
2
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(2 citation statements)
references
References 9 publications
0
2
0
Order By: Relevance
“…This study attempted to find researches that present an itemized interpretation of SSIM indices to determine the most acceptable index value but was unsuccessful in this task. The researchers thus resorted to an indirect method by compiling several studies attempting to relate SSIM with subjective human visual perception (Lee & Lim, 2016;Wang, Wang, Liao & Lim, 2008;Lu, Bi & Wang, 2010;Yu & Liu, 2011) and obtaining the indices calculated from images with salt-andpepper noise, as this item is given low degradation mean opinion scores (DMOS) by human observers (Minamoto & Ohmura, 2014). From these studies, it was inferred that an SSIM rating of 0.8 can be considered as an acceptable similarity measure so that noisy images will fall below it.…”
Section: Analysis Of Resultsmentioning
confidence: 99%
“…This study attempted to find researches that present an itemized interpretation of SSIM indices to determine the most acceptable index value but was unsuccessful in this task. The researchers thus resorted to an indirect method by compiling several studies attempting to relate SSIM with subjective human visual perception (Lee & Lim, 2016;Wang, Wang, Liao & Lim, 2008;Lu, Bi & Wang, 2010;Yu & Liu, 2011) and obtaining the indices calculated from images with salt-andpepper noise, as this item is given low degradation mean opinion scores (DMOS) by human observers (Minamoto & Ohmura, 2014). From these studies, it was inferred that an SSIM rating of 0.8 can be considered as an acceptable similarity measure so that noisy images will fall below it.…”
Section: Analysis Of Resultsmentioning
confidence: 99%
“…Currently, blackness determination relies on physical blackness meters, and there is a lack of a method to automatically determine blackness in X-ray weld image quality evaluation. Yu M [1] proposed a new machine learning-based full-reference image quality evaluation model, which includes four types of features: chromaticity, gradient, contrast sensitivity function, and Gaussian difference band, by fully simulating the human eye visual system and brain mechanism.Hu [2] incorporated the edge features and texture features of the image based on the weighted local entropy algorithm, and the experimental results showed that the method was consistent with subjective evaluation results with a high degree of consistency. Gao M [3] broke through the classical algorithmic framework based on local information and proposed a framework based on nonlocal information, and constructed a nonlocal gradient-based image quality evaluation algorithm within this framework.…”
Section: Introductionmentioning
confidence: 99%