New Structural Similarity Measure for Image Comparison

Abstract

Subjective quality measures based on Human Visual System for images do not agree well with well-known metrics such as Mean Squared Error and Peak Signal to Noise Ratio. Recently, Structural Similarity Measure (SSIM) has received acclaim due to its ability to produce results on a par with Human Visual System. However, experimental results indicate that noise and blur seriously degrade the performance of the SSIM metric. Furthermore, despite SSIM's popularity, it does not provide adequate insight into how it handles 'structural similarity' of images. We propose a structural similarity measure based on approximation level of a given Discrete Wavelet Decomposition that evaluates moment invariants to capture the structural similarity with superior results over SSIM. Abstract. Subjective quality measures based on Human Visual System for images do not agree well with well-known metrics such as Mean Squared Error and Peak Signal to Noise Ratio. Recently, Structural Similarity Measure (SSIM) has received acclaim due to its ability to produce results on a par with Human Visual System. However, experimental results indicate that noise and blur seriously degrade the performance of the SSIM metric. Furthermore, despite SSIM's popularity, it does not provide adequate insight into how it handles 'structural similarity' of images. We propose a structural similarity measure based on approximation level of a given Discrete Wavelet Decomposition that evaluates moment invariants to capture the structural similarity with superior results over SSIM.

Modify Manhattan Distance for Image Similarity

“…An old statistical measure that has been widely used to detect image similarity is themean squared error (MSE) [3,4,5].…”