Blind Assessment of Wavelet-Compressed Images Based on Subband Statistics of Natural Scenes
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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.Keywords:
Distortion (music)
Similarity (geometry)
Correlation power analysis (CPA) is a side-channel attack (SCA) which exploits the information leaked through the power supply current and voltage, or the electromagnetic emissions of the attacked digital system. It uses statistical analysis of a large number of power supply measurements to retrieve the secrets of the digital system. Correlation power analysis uses a number of hypothetical secret keys which are correlated to the measurements of the attacked system. Usually correlation power analysis uses the Pearson correlation coefficient, but the intermediary values and the power supply measurements can have a nonlinear relationship. The paper investigates the application of the distance correlation in the correlation power analysis and compares it to the Pearson correlation coefficient. The comparison is based on a side-channel attack on a multiplication operation of an input message and a secret key. The results of the comparison show that the distance correlation achieves a higher prominence of the correct secret key than the Pearson correlation coefficient.
Distance correlation
Power analysis
Fisher transformation
Correlation ratio
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Pearson’s correlation coefficient is used to describe dependence between random variables X and Y . In some practical situations, however, we have strong correlation for some values X and/or Y and no correlation for other values ofX and Y . To describe such a local dependence, we come up with a natural localized version of Pearson’s correlation coefficient. We also study the properties of the newly defined localized coefficient. 1 Formulation of the Problem Pearson’s correlation coefficient: reminder. To describe relation between two random variables X and Y , Pearson’s correlation coefficient r is often used. This coefficient is defined as r[X,Y ] def = C[X,Y ] σ[X] · σ(Y ) , (1)
Fisher transformation
Distance correlation
Correlation ratio
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The similarity weight, the pearson's correlation coefficient, which is used in the recommender system has a weak point that it cannot predict all of the prediction value. The similarity weight, the vector similarity, has a weak point of the high MAE although the prediction coverage using the vector similarity is higher than that using the pearson's correlation coefficient. The purpose of this study is to suggest how to raise the prediction coverage. Also, the MAE using the suggested method in this study was compared both with the MAE using the pearson's correlation coefficient and with the MAE using the vector similarity, so was the prediction coverage. As a result, it was found that the low of the MAE in the case of using the suggested method was higher than that using the pearson's correlation coefficient. However, it was also shown that it was lower than that using the vector similarity. In terms of the prediction coverage, when the suggested method was compared with two similarity weights as I mentioned above, it was found that its prediction coverage was higher than that pearson's correlation coefficient as well as vector similarity.
Similarity (geometry)
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A database of DNA fingerprint profiles from permanently established human and animal cell lines was prepared with a computer program originally designed for numerical taxonomy of bacteria. Identifications of cell line DNA profiles were performed, both by the Pearson product-moment correlation coefficient and by band matching. Under the conditions used the Pearson product-moment correlation coefficient was consistently more reliable.
DNA profiling
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The Pearson product-moment correlation coefficient is being used to evaluate the similarity of the high-performance liquid chromatographic fingerprints of traditional Chinese medicine (TCM) in China. It is confirmed that a large range of peak areas produced the wrong results. A new algorithm concerning weighted Pearson product-moment correlation coefficient is proposed in this article. The results for both real cases and simulated data sets show that the weighted Pearson product-moment correlation coefficients allow relatively larger differences for large values, smaller differences for small values, and more reliable results than the unweighted Pearson product-moment correlation coefficients. Weight selection depends on the specific scientific problem.
Similarity (geometry)
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Correlation methods are indispensable in the study of the linear relationship between two variables. However, many researchers often adopt inappropriate correlation methods in the study of linear relationships which usually leads to unreliable results. Recurrently, most researchers ignorantly employ the Pearson method in a dataset that contained outliers, instead of more appropriate correlation methods such as Spearman, Kendall Tau, Median and Quadrant which might be suitable in the calculation of correlation coefficient in the presence of influential outliers. It is noted that the accuracy of estimation of correlation coefficients under outliers has been a long-standing problem for methodological researchers. This is due to low knowledge of correlation methods and their assumptions which have led to inappropriate application of correlation methods in research analysis. Five different methods of estimating correlation coefficients in the presence of influential outlier (contaminated data) were considered: Pearson Correlation Coefficient, Spearman Correlation Coefficient, Kendall Tau Correlation Coefficient, Median Correlation Coefficient and Quadrant Correlation Coefficient.
Fisher transformation
Correlation ratio
Linear correlation
Partial correlation
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Aim To study the reason of the insensitiveness of Pearson preduct-moment correlation coefficient as a similarity measure and the method to improve its sensitivity. Methods Experimental and simulated data sets were used. Results The distribution range of the data sets influences the sensitivity of Pearson product-moment correlation coefficient. Weighted Pearson product-moment correlation coefficient is more sensitive when the range of the data set is large. Conclusion Weighted Pearson product-moment correlation coefficient is necessary when the range of the data set is large.
Fisher transformation
Similarity (geometry)
Correlation ratio
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The correlation coefficient is one of the most commonly used statistical measures in all branches of statistics. The empirical evidence shows that this correlation coefficient is sufficiently non-robust against outliers. The aim of this study is to compare the performance of the estimator of correlation coefficient. In this study, Pilot-plant data was considered at first stage. Second stage of this study, the simulation data were generated based on normal and uniform distribution at its four contaminated form. The methods of analysis used in this study were Pearson’s correlation coefficient and An Absolute Value correlation coefficient. It can be conclude that an Absolute Value correlation coefficient performs well and more robust compared to Pearson’s correlation coefficient in existence of outliers. Then we investigated the bias, standard error (SE) and root mean square error (RMSE) to judge their performance. The result shows that an Absolute Value performs better than Pearson’s correlation coefficient. In general An Absolute Value correlation coefficient appears to be a good estimator because it has the lowest values of bias, standard error and RMSE.
Fisher transformation
Correlation ratio
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Aim To study the reason of the insensitiveness of Pearson product-moment correlation coefficient as a similarity measure and the method to improve its sensitivity. Methods Experimental and simulated data sets were used. Results The distribution range of the data sets influences the sensitivity of Pearson product-moment correlation coefficient. Weighted Pearson product-moment correlation coefficient is more sensitive when the range of the data set is large. Conclusion Weighted Pearson product-moment correlation coefficient is necessary when the range of the data set is large.
Similarity (geometry)
Correlation ratio
Fisher transformation
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Epilepsy is generally considered as a collection of neurological disorders. Electroencephalography (EEG), a useful measure for analyzing the brain's electrical activity, has been widely used for the diagnosis of epileptic seizures. In an EEG diagnosis report, synchrony of epileptiform discharges should be included in the description. Correlation coefficient analysis could be used to measure the synchrony feature. However, the existing correlation coefficient overlooks the importance of epileptiform discharges in EEG data of epilepsy. In this paper, in order to tackle this problem, we propose a novel correlation coefficient to measure the synchrony feature. This method combines the attention mechanism with the correlation coefficient. We use the first-order difference to assign the attention weights and apply the weight to the Pearson's correlation coefficient. The first-order difference can highlight the high-frequency and high-amplitude waveforms in the original time series. Therefore, epileptiform discharges could play a more important role in the calculation of the correlation coefficient. We collected the EEG of epileptic patients during the interictal period and labeled the epileptiform discharge segments for experimental tests. In our case study, the Pearson's correlation coefficient with attention weights gave better results than the direct use of Pearson's correlation coefficient.
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