期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：24
出版社：Journal of Theoretical and Applied
摘要：In this work we present a study on the performance of signal similarity measures under non-Gaussian noise. Pink noise has been considered, with 1/f power spectral density. This kind of noise has been generated by filtering Gaussian noise through an FIR filter. One-dimensional and two-dimensional signals have been considered. We tested 2D image similarity using the well-known similarity measures: Structural Similarity Index Measure (SSIM), modified Feature-based Similarity Measure (MFSIM), and Histogram-based Similarity Measure (HSSIM). Also, we tested 1D similarity measures: Cosine Similarity, Pearson Correlation, Tanimoto similarity, and Angular similarity. Results show that HSSIM and MFSIM outperform SSIM in low PSNR under pink noise and Gaussian noise. For 1D similarity, it is shown that Cosine Similarity and Pearson Correlation outperform other 1D similarity, especially at low SNR.
