Blind image quality assessment by relative gradient statistics and adaboosting neural network

Liu, Lixiong; Hua, Yi; Zhao, Qingjie; Huang, Hua; Bovik, Alan C.

doi:10.1016/j.image.2015.10.005

Cited by 230 publications

(149 citation statements)

References 54 publications

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“…NR Image/Video Quality Assessment (IQA/VQA) generally rely on feature extracting and feature learning based methods. A good overview of such techniques can be found in [34,35].…”

Section: A Visual Quality Evaluation Methods For Telemedicine Applicatmentioning

confidence: 99%

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A visual quality evaluation method for telemedicine applications

Nasr

Martini

2017

Signal Processing: Image Communication

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Ratio (PSNR) and the Structural SIMilarity index metric (SSIM). Comparisons with subjective results interms of Differential Mean Opinion Scores (DMOS) are also presented. We show that the presented method, not needing the original frame to predict the quality, achieves a high correlation with subjective results (more than 0.9) for the different quality metrics used. We also present relationships between the quality derived via the logo and via the original frame and we assess the overhead in data transmission resulting from the compressed logo data and its protection overhead.Index Terms-Visual quality evaluation, medical ultrasound, objective quality metrics, subjective quality metrics, medical quality of service/experience, wireless telemedicine

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“…NR Image/Video Quality Assessment (IQA/VQA) generally rely on feature extracting and feature learning based methods. A good overview of such techniques can be found in [34,35].…”

Section: A Visual Quality Evaluation Methods For Telemedicine Applicatmentioning

confidence: 99%

“…While the correlation with subjective results is reasonable, this is not high enough for medical images. All NR and RR techniques reported in the literature vary in complexity and processing requirements [34,35] and may not particularly answer the needs in a telemedicine application.…”

Section: A Visual Quality Evaluation Methods For Telemedicine Applicatmentioning

confidence: 99%

A visual quality evaluation method for telemedicine applications

Nasr

Martini

2017

Signal Processing: Image Communication

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“…This measure discriminates blur and Gaussian noise. In [9], authors presented a new model, called Oriented Gradients Image Quality Assessment (OG-IQA), showing highly competitive image quality prediction performance as compared with other quality measures. The LIVE image database [5] was used for performance evaluation of the model.…”

Section: Related Workmentioning

confidence: 99%

“…L L L Evaluated level (9) where L2 is the evaluated level for parameter 2, L10 for parameter 10, and L18 for parameter 18. Additionally, we tested the performance of the proposed quality measure using Pearson's and Spearman's correlation between DMOS (Difference MOS) LIVE dataset scores for noise degradation, MOS VCL@FER dataset scores for blur degradation and results of the proposed measure.…”

Section: Level Of Blurriness and Gaussian Noisementioning

confidence: 99%

Evaluation of Blur and Gaussian Noise Degradation in Images Using Statistical Model of Natural Scene and Perceptual Image Quality Measure

Bjelopera¹,

Dumic²,

Grgić³

2017

RADIOENGINEERING

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“…All features considered in this paper are extracted from nine commonly used learningbased NR- [22]. A reason of the choice of those trial algorithms is motivated by the fact that the code of all of them is publicly available.…”

Section: Selected Featuresmentioning

confidence: 99%

No-Reference Learning-Based and Human Visual-Based Image Quality Assessment Metric

Charrier

Saadane

Fernández-Maloigne

2017

Image Analysis and Processing - ICIAP 2017

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Abstract. With the rapid growth of multimedia applications and technologies, objective image quality assessment (IQA) became a topic of fundamental interest. No-Reference (NR) IQA algorithms are more suitable to real-world applications where the original image is not available. In order to be more consistent with human perception, this paper proposes a new NR-IQA metric where the input image is firstly decomposed to several frequency sub-bands which mimic the human visual system (HVS). Then, the statistical features are extracted from these frequency bands and used to fit a multivariate Gaussian distribution (MVGD). Finally, the model obtained by training predicts the quality of the input image. Experimental results demonstrate the method effectiveness and show its robustness when tested by different databases. Moreover, the predicted quality is more consistent with human perception.

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Blind image quality assessment by relative gradient statistics and adaboosting neural network

Cited by 230 publications

References 54 publications

A visual quality evaluation method for telemedicine applications

A visual quality evaluation method for telemedicine applications

Evaluation of Blur and Gaussian Noise Degradation in Images Using Statistical Model of Natural Scene and Perceptual Image Quality Measure

No-Reference Learning-Based and Human Visual-Based Image Quality Assessment Metric

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