Coded Permutation Entropy: A Measure for Dynamical Changes Based on the Secondary Partitioning of Amplitude Information An improved permutation entropy (PE) algorithm named coded permutation entropy (CPE) is proposed in this paper to optimize the problems existing in PE based on the secondary partitioning. The principle of CPE algorithm is given, and the performance of it for dynamical change detection is analyzed using synthetic signal, logistic map and Lorenz map. The detection ability of CPE algorithm in different signaltonoise ratios (SNR) is studied and the algorithm complexity is discussed. The results show that CPE can accurately capture minor feature information and amplify the detection results of dynamical changes compared with PE, weighted permutation entropy (WPE) and amplitudeaware permutation entropy (AAPE), but it has less robustness to noise and requires a higher computation cost than the others. Finally, we use the new algorithm to analyze the rolling bearing fault signals. The application of actual signals illustrates that CPE performs better in detecting abnormal pulse of the rolling bearing when the embedded dimension is small. From all the analyses in this paper, we find that CPE has a better performance for dynamical change detection compared with the other three algorithms when there is a larger repetition rate of permutation pattern in the position sequences.

FRIQA  Permutation Entropy Deviation Index In this work, a new objective image quality assessment (IQA) framework, based on the working principle of permutation entropy (PE) is proposed. The framework is titled as Permutation Entropy Deviation Index (PEDI). The idea is to design an IQA framework that should be highly accurate as well as computationally efficient; making it viable to be used with different image processing applications, for visual quality assessments. The proposed model make use of the PE that helps in detecting and visualizing changes related to structures with correlation between successive samples instead of considering magnitudes of the signal. Thus, the proposed approach uses this property to efficiently predict image quality. The PE exploits the global variations in the local quality map for image quality assessment. With standard deviation as the pooling strategy, it is noted that permutation entropy between reference and distorted images can predict image quality with high measures of accuracy. Experimental results on subjective database, CSIQ, have shown that the proposed model outperforms most of existing SOTA image quality assessment models and highly correlates with subjective judgements.