scholarly journals An Integration of Deep Network with Random Forests Framework for Image Quality Assessment in Real-Time

2021 ◽  
Author(s):  
Zahi Al Chami ◽  
Chady Abou Jaoude ◽  
Richard Chbeir
Keyword(s):  
Real Time ◽  
Correlation Coefficient ◽  
Random Forests ◽  
Image Quality Assessment ◽  
Rank Order ◽  
Pearson Correlation ◽  
Deep Network ◽  
Wide Range ◽  
Benchmark Datasets ◽  
Efficient Data

In recent years, data providers are generating and streaming a large number of images. More particularly, processing images that contain faces have received great attention due to its numerous applications, such as entertainment and social media apps. The enormous amount of images shared on these applications presents serious challenges and requires massive computing resources to ensure efficient data processing. However, images are subject to a wide range of distortions in real application scenarios during the processing, transmission, sharing, or combination of many factors. So, there is a need to guarantee acceptable delivery content, even though some distorted images do not have access to their original version. In this paper, we present a framework developed to estimate the images' quality while processing a large number of images in real-time. Our quality evaluation is measured using an integration of a deep network with random forests. In addition, a face alignment metric is used to assess the facial features. Experimental results have been conducted on two artificially distorted benchmark datasets, LIVE and TID2013. We show that our proposed approach outperforms the state-of-art methods, having a Pearson Correlation Coefficient (PCC) and Spearman Rank Order Correlation Correlation Coefficient (SROCC) with subjective human scores of almost 0.942 and 0.931 while minimizing the processing time from 4.8ms to 1.8ms.

scholarly journals The Adoption Rate of Isfahan University of Medical Sciences with Components of the Learning Organization from the Perspective of Senior and Middle University Managers

2019 ◽  
Author(s):  
Monireh Sadeqi Jabali ◽  
Hamidreza Tadayon ◽  
Sharifeh Monemian
Keyword(s):  
Correlation Coefficient ◽  
Learning Organization ◽  
Team Learning ◽  
Middle Managers ◽  
Pearson Correlation ◽  
Compliance Rate ◽  
Shared Vision ◽  
Medical Sciences ◽  
Wide Range ◽  
The Individual

Background: Universities of Medical Sciences are required to take steps to implement dimensions of the learning organization due to their wide-range of activities and their role in maintaining and improving the community health. This study was conducted to determine the compliance of Isfahan University of Medical Sciences with the components of learning organization.  Methods: This descriptive-analytic study was conducted among all senior and middle managers of Isfahan University of Medical Sciences at the Central headquarters, colleges, and hospitals. For data collection, the standard Neife Learning Organization Questionnaire (2001) was applied .Validity of the questionnaire was evaluated in terms of content validity based on the experts' opinion and the questionnaire's reliability was confirmed by calculating the Cronbach's alpha coefficient of 0.89. Data were analyzed using descriptive and analytical statistics such as Pearson correlation coefficient, t-test, and Spearman correlation coefficient through SPSS 20. Results: The compliance rate of Isfahan University of Medical Sciences with components of the learning organization was 72.60 from the viewpoint of the senior and middle managers of this university in total. The highest mean score was attributed to the individual skills (77.60) and the lowest mean scores were related to the components of mental models (67.03), team learning (70.70), and shared vision (72.20), respectively. Independent t-tests showed that the total mean score of adoption with the learning organization and scores of the shared vision, team learning, and systematic thinking components were significantly higher in male than female managers (p < 0.05). Conclusion: The Isfahan University of Medical Sciences' compliance with characteristics of the learning organization is at a desirable level. Managers and authorities can achieve the highest level of learning organization by improving all components of the learning organization, especially the mental model, team learning, and shared vision.

A No-Reference Image Quality Comprehensive Assessment Method

2020 ◽  
pp. 2154011
Author(s):  
Yuan-Yuan Fan ◽  
Ying-Jun Sang
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Correlation Coefficient ◽  
Image Quality Assessment ◽  
Rank Order ◽  
Comprehensive Assessment ◽  
Assessment Model ◽  
Reference Image ◽  
Human Visual Perception ◽  
Function Model

On the basis of the research status of image quality comprehensive assessment, a no-reference image quality comprehensive assessment function model is proposed in this paper. First, the image quality is classified as contrast, sharpness, and signal-to-noise ratio (SNR), and the interrelation of each assessment index is researched and analyzed; second, the weights in the comprehensive assessment model are studied when only contrast, sharpness, and SNR are changed. Finally, on the basis of studying each kind of distortion separately, and considering the different types of image distortion, we studied how to determine the weights of each index in the comprehensive image quality assessment. The results show that the no-reference image quality comprehensive assessment function model proposed in this paper can better fit human visual perception, and it has a good correlation with Difference Mean Opinion Score (DMOS). Correlation Coefficient (CC) reached 0.8331, Spearman Rank Order Correlation Coefficient (SROCC) reached 0.8206, Mean Absolute Error (MAE) was only 0.0920, Root Mean Square Error (RMSE) was only 0.1122, Outlier Ratio (OR) was only 0.0365. The method proposed in this paper can be applied to photoelectric measurement equipment television system and give an accurate and reliable quality assessment to no reference television images.

Blind Assessment of Wavelet-Compressed Images Based on Subband Statistics of Natural Scenes

2018 ◽  
pp. 1322-1337
Author(s):  
Yingchun Guo ◽  
Gang Yan ◽  
Cuihong Xue ◽  
Yang Yu
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Correlation Coefficient ◽  
Image Quality Assessment ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Subjective Assessment ◽  
Natural Scenes ◽  
Assessment Scores ◽  
Compressed Images

This paper presents a no-reference image quality assessment metric that makes use of the wavelet subband statistics to evaluate the levels of distortions of wavelet-compressed images. The work is based on the fact that for distorted images the correlation coefficients of the adjacent scale subbands change proportionally with respect to the distortion of a compressed image. Subband similarity is used in this work to measure the correlations of the adjacent scale subbands of the same wavelet orientations. The higher the image quality is (i.e., less distortion), the greater the cosine similarity coefficient will be. Statistical analysis is applied to analyze the performance of the metric by evaluating the relationship between the human subjective assessment scores and the subband cosine similarities. Experimental results show that the proposed blind method for the quality assessment of wavelet-compressed images has sufficient prediction accuracy (high Pearson Correlation Coefficient, PCCs), sufficient prediction monotonicity (high Spearman Correlation Coefficient SCCs) and sufficient prediction consistency (low outlier ratios) and less running time. It is simple to calculate, has a clear physical meaning, and has a stable performance for the four image databases on which the method was tested.

scholarly journals The role of elastography in the assessment of chronic liver disease in children

2019 ◽  
Vol 19 (3) ◽  
pp. 2806-2311
Author(s):  
Süleyman Sönmez ◽  
Merve Boşat ◽  
Nihal Yurtseven ◽  
Eray Yurtseven
Keyword(s):  
Liver Disease ◽  
Liver Biopsy ◽  
Chronic Liver Disease ◽  
Real Time ◽  
Correlation Coefficient ◽  
Hepatic Fibrosis ◽  
Pearson Correlation ◽  
Chronic Liver ◽  
Liver Biopsies

Background: Conventional ultrasonography is a method preferred for the investigation of chronic liver diseases in pediatric groups, as it is non-invasive, cheap, feasible and available. The purpose of this study is to present the role of Share-wave Elastography (SWE) in terms of diagnostic value in children diagnosed with “chronic liver disease.”Methods: We studied patients who had been diagnosed with chronic liver disease between March 2012-September 2015, and who had undergone liver biopsy and had their pathology results, compared with 26 healthy subjects. Statistical analysis was performed with IBM SPSS Statistics for Windows, Version 20.0. “Pearson Correlation Analysis” was performed in order to measure the relationship between elastography values and Brunt level.Results: This study had 107 subjects in total, consisting of 81 patients between 0-204 months of age Pearson correlation coefficient level was determined as r = 0.644. Since the correlation coefficient is positive, there is a same-directional relationship between Elastography level and Brunt degree. This means that while one of the variables is increasing, the other one will also increase.Conclusion: Since it is known that development of hepatic fibrosis is a dynamic process, and that many hepatic fibrosis etiologies are known to continue throughout the course of life, the application of Real time SWE method instead of repeated liver biopsies on patients is a much simpler and smart method. Increasing the clinical use of Real Time SWE method with future studies might provide an opportunity for preventing unnecessary liver biopsies since the patients are evaluated in a shorter time and in a cost-effective manner.Keywords: Shear-Wave Elastography, Brunt degree, chronic liver disease, liver biopsy.

scholarly journals Real-time Prediction of the Daily Incidence of COVID-19 in 215 Countries and Territories Using Machine Learning: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yuanyuan Peng ◽  
Xinjian Chen ◽  
Yibiao Rong ◽  
Chi Pui Pang ◽  
Xinjian Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Correlation Coefficient ◽  
Pearson Correlation ◽  
Google Trends ◽  
Pearson Correlation Coefficient ◽  
Spearman Correlation ◽  
Time Prediction ◽  
Search Volume ◽  
The Mean

BACKGROUND Advanced prediction of the daily incidence of COVID-19 can aid policy making on the prevention of disease spread, which can profoundly affect people's livelihood. In previous studies, predictions were investigated for single or several countries and territories. OBJECTIVE We aimed to develop models that can be applied for real-time prediction of COVID-19 activity in all individual countries and territories worldwide. METHODS Data of the previous daily incidence and infoveillance data (search volume data via Google Trends) from 215 individual countries and territories were collected. A random forest regression algorithm was used to train models to predict the daily new confirmed cases 7 days ahead. Several methods were used to optimize the models, including clustering the countries and territories, selecting features according to the importance scores, performing multiple-step forecasting, and upgrading the models at regular intervals. The performance of the models was assessed using the mean absolute error (MAE), root mean square error (RMSE), Pearson correlation coefficient, and Spearman correlation coefficient. RESULTS Our models can accurately predict the daily new confirmed cases of COVID-19 in most countries and territories. Of the 215 countries and territories under study, 198 (92.1%) had MAEs &lt;10 and 187 (87.0%) had Pearson correlation coefficients &gt;0.8. For the 215 countries and territories, the mean MAE was 5.42 (range 0.26-15.32), the mean RMSE was 9.27 (range 1.81-24.40), the mean Pearson correlation coefficient was 0.89 (range 0.08-0.99), and the mean Spearman correlation coefficient was 0.84 (range 0.2-1.00). CONCLUSIONS By integrating previous incidence and Google Trends data, our machine learning algorithm was able to predict the incidence of COVID-19 in most individual countries and territories accurately 7 days ahead.

scholarly journals A Seismic Demand Model for Bridges Based on IM and EDP Parameter Matching and Bidirectional Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Shuai Wang ◽  
Shuai Song ◽  
Gang Wu
Keyword(s):  
Correlation Coefficient ◽  
Seismic Vulnerability ◽  
Pearson Correlation ◽  
Vulnerability Analysis ◽  
Seismic Demand ◽  
Demand Model ◽  
Demand Models ◽  
Wide Range ◽  
Seismic Demand Model ◽  
Seismic Vulnerability Analysis

The accuracy of seismic demand models in seismic vulnerability analysis of structures or components mainly depends on the seismic intensity measures (IMs) and engineering demand parameters (EDPs). This paper proposes a novel method to obtain the optimal seismic demand model for the seismic vulnerability analysis of bridges. The method obtains the IM-EDP combination by matching all IMs and EDPs within a wide range one by one, considering the contribution of multiple IM parameters to the seismic response of the structure and avoiding the blindness of EDP selection. The IM is determined by calculating Pearson correlation coefficient and partial correlation coefficient, controlling the correlation between EDP and IM (or IMs) to a minimum to reduce the multicollinearity within the vector IMs and avoid ill-conditioned models. The optimal seismic demand model is obtained by inspecting the scatter plot and residual plot of suboptimal seismic demand models determined from all combinations by guaranteeing efficiency and sufficiency. The efficiency of seismic demand models is guaranteed by controlling the root mean square error (RMSE) and the coefficient of determination (R2). The sufficiency of models is guaranteed by controlling the slope of fitted line. A continuous rigid frame bridge with double thin-walled piers is used as a case study and a dynamic time-history analysis is performed to obtain the seismic vulnerability of bridge with the proposed method. The results show that the proposed method is feasible and ideally suited for optimizing seismic demand model.

scholarly journals No reference Image Quality Measure for Hazy Images

2020 ◽  
Vol 13 (6) ◽  
pp. 460-471
Author(s):  
Ahmed Hashim ◽  
◽  
Hazim Daway ◽  
Hana kareem ◽  
◽  
...  
Keyword(s):  
Image Quality ◽  
Correlation Coefficient ◽  
Image Quality Assessment ◽  
Pearson Correlation ◽  
Human Perception ◽  
Reference Image ◽  
Special Device ◽  
Image Quality Measure ◽  
Subjective Study

Haze causes the degradation of image quality. Thus, the quality of the haze must be estimated. In this paper, we introduce a new method for measuring the quality of haze images using a no-reference scale depending on color saturation. We calculate the probability for a saturation component. This work also includes a subjective study for measuring image quality using human perception. The proposed method is compared with other methods as, entropy, Naturalness Image Quality Evaluator (NIQE), Haze Distribution Map based Haze Assessment (HDMHA), and no reference image quality assessment by using Transmission Component Estimation (TCE). This done by calculating the correlation coefficient between non-reference measures and subjective measure, the results show that the proposed method has a high correlation coefficient values for Pearson correlation coefficient (0.8923), Kendall (0.7170), and Spearman correlation coefficient (0.8960). The image database used in this work consists of 70 hazy images captured by using a special device, design to capture haze image. The experiment on haze database is consistent with the subjective experiment.

scholarly journals SMOS near-real-time soil moisture product: processor overview and first validation results

2017 ◽  
Vol 21 (10) ◽  
pp. 5201-5216 ◽  
Author(s):  
Nemesio J. Rodríguez-Fernández ◽  
Joaquin Muñoz Sabater ◽  
Philippe Richaume ◽  
Patricia de Rosnay ◽  
Yann H. Kerr ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Surface Soil ◽  
Pearson Correlation ◽  
Weather Prediction ◽  
European Space Agency ◽  
Retrieval Algorithm ◽  
Reference Network ◽  
Wide Range ◽  
Level 2

Abstract. Measurements of the surface soil moisture (SM) content are important for a wide range of applications. Among them, operational hydrology and numerical weather prediction, for instance, need SM information in near-real-time (NRT), typically not later than 3 h after sensing. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure SM from space. The ESA Level 2 SM retrieval algorithm is based on a detailed geophysical modelling and cannot provide SM in NRT. This paper presents the new ESA SMOS NRT SM product. It uses a neural network (NN) to provide SM in NRT. The NN inputs are SMOS brightness temperatures for horizontal and vertical polarizations and incidence angles from 30 to 45°. In addition, the NN uses surface soil temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS). The NN was trained on SMOS Level 2 (L2) SM. The swath of the NRT SM retrieval is somewhat narrower (∼ 915 km) than that of the L2 SM dataset (∼ 1150 km), which implies a slightly lower revisit time. The new SMOS NRT SM product was compared to the SMOS Level 2 SM product. The NRT SM data show a standard deviation of the difference with respect to the L2 data of < 0.05 m3 m−3 in most of the Earth and a Pearson correlation coefficient higher than 0.7 in large regions of the globe. The NRT SM dataset does not show a global bias with respect to the L2 dataset but can show local biases of up to 0.05 m3 m−3 in absolute value. The two SMOS SM products were evaluated against in situ measurements of SM from more than 120 sites of the SCAN (Soil Climate Analysis Network) and the USCRN (US Climate Reference Network) networks in North America. The NRT dataset obtains similar but slightly better results than the L2 data. In summary, the NN SMOS NRT SM product exhibits performances similar to those of the Level 2 SM product but it has the advantage of being available in less than 3.5 h after sensing, complying with NRT requirements. The new product is processed at ECMWF and it is distributed by ESA and via the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) multicast service (EUMETCast).

Blind Assessment of Wavelet-Compressed Images Based On Subband Statistics of Natural Scenes

2014 ◽  
Vol 6 (1) ◽  
pp. 26-43
Author(s):  
Yingchun Guo ◽  
Gang Yan ◽  
Cuihong Xue ◽  
Yang Yu
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Correlation Coefficient ◽  
Image Quality Assessment ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Subjective Assessment ◽  
Reference Image ◽  
Natural Scenes ◽  
Compressed Images

This paper presents a no-reference image quality assessment metric that makes use of the wavelet subband statistics to evaluate the levels of distortions of wavelet-compressed images. The work is based on the fact that for distorted images the correlation coefficients of the adjacent scale subbands change proportionally with respect to the distortion of a compressed image. Subband similarity is used in this work to measure the correlations of the adjacent scale subbands of the same wavelet orientations. The higher the image quality is (i.e., less distortion), the greater the cosine similarity coefficient will be. Statistical analysis is applied to analyze the performance of the metric by evaluating the relationship between the human subjective assessment scores and the subband cosine similarities. Experimental results show that the proposed blind method for the quality assessment of wavelet-compressed images has sufficient prediction accuracy (high Pearson Correlation Coefficient, PCCs), sufficient prediction monotonicity (high Spearman Correlation Coefficient SCCs) and sufficient prediction consistency (low outlier ratios) and less running time. It is simple to calculate, has a clear physical meaning, and has a stable performance for the four image databases on which the method was tested.

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