| Title |
Blind image quality assessment via probabilistic latent semantic analysis
|
|---|---|
| Published in |
SpringerPlus, October 2016
|
| DOI | 10.1186/s40064-016-3400-1 |
| Pubmed ID | |
| Authors |
Xichen Yang, Quansen Sun, Tianshu Wang |
| Abstract |
We propose a blind image quality assessment that is highly unsupervised and training free. The new method is based on the hypothesis that the effect caused by distortion can be expressed by certain latent characteristics. Combined with probabilistic latent semantic analysis, the latent characteristics can be discovered by applying a topic model over a visual word dictionary. Four distortion-affected features are extracted to form the visual words in the dictionary: (1) the block-based local histogram; (2) the block-based local mean value; (3) the mean value of contrast within a block; (4) the variance of contrast within a block. Based on the dictionary, the latent topics in the images can be discovered. The discrepancy between the frequency of the topics in an unfamiliar image and a large number of pristine images is applied to measure the image quality. Experimental results for four open databases show that the newly proposed method correlates well with human subjective judgments of diversely distorted images. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 5 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 2 | 40% |
| Lecturer | 1 | 20% |
| Student > Bachelor | 1 | 20% |
| Unknown | 1 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 2 | 40% |
| Engineering | 1 | 20% |
| Unknown | 2 | 40% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 November 2016.
All research outputs
#18,482,034
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Outputs from SpringerPlus
#1,261
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#242,070
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Outputs of similar age from SpringerPlus
#109
of 151 outputs
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