Quantifying image similarity using measure of enhancement by entropy

Abstract

Measurement of image similarity is important for a number of image processing applications. Image similarity assessment is closely related to image quality assessment in that quality is based on the apparent differences between a degraded image and the original, unmodified image. Automated evaluation of image compression systems relies on accurate quality measurement. Current algorithms for measuring similarity include mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). They have some limitations: such as consistent, accuracy and incur greater computational cost. In this paper, we show that a modified version of the measurement of enhancement by entropy (EME) can be used as an image similarity measure, and thus an image quality measure. Until now, EME has generally been used to measure the level of enhancement obtained using a given enhancement algorithm and enhancement parameter. The similarity-EME (SEME) is based on the EME for enhancement. We will compare SEME to existing measures over a set of images subjectively judged by humans. Computer simulations have demonstrated its promise through a set of examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG.