Modify Manhattan Distance for Image Similarity
new measurement for image similarity
DOI:
https://doi.org/10.31580/ojst.v2i4.984Keywords:
Image Structural Similarity, Image Similarity, Mahattendistance, Standarddevation, Gaussian NoiseAbstract
A New measure is proposed for assessing the similarity among gray-scale images. The well-known Structural Similarity Index Measure (SSIM) has been designed using a statistical approach that fails under significant noise (lowPSNR). The proposed measure, denoted by Manhattan distance and STD, uses a combination of two parts: the first part is the Geometric method, while the second part is based on the statistical feature. The concept of manhattan distance is used in the geometric part. The new measure shows the advantages of statistical approaches and geometric approaches. The proposed similarity method is an outcome for the human face. The novel measure outperforms the classical SSIM in detecting image similarity at low PSNR, with a significant difference in performance. AMS subject classification:
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