Abstract
We present an information-theoretic approach for st ructural similarity for assessing gray scale image quality. The structural similarity measure SSIM, pr oposed in 2004, has been successflly used and verfi ed. SSIM is based on statistical similarity between the two images. However, SSIM can produce confusing results in some cases where it may give a non-trivi al amount of similarity for two different images. A lso, SSIM cannot perform well (in detecting similarity o r dissimilarity) at low peak signal to noise ratio (PSNR). In this study, we present a novel image similarity measure, HSSIM, by using information - theoretic technique based on joint histogram. The proposed method has been tested under Gaussian noise. Simulati on results show that the proposed measure HSSIM outperforms statistical similarity SSIM by ability to det ect similarity under very low PSNR. The average differe nce is about 20dB.
Highlights
The above-mentioned similarity measures are all analysed based on statistical moments, which we will study in this study versus information-theoretic approach in order to test similarity
In this paper we focus on image information-theoretic properties, the joint histogram and propose the following image-dependent measure
After we implemented the joint histogram-based measure (HSSIM) as per Equation 4 and structural similarity measure (SSIM) as per Equation 1, we tested their performance of detecting similarity under noisy conditions, when the other image is corrupted with Gaussian noise
Summary
Between the reference image and the distorted version. Image similarity measurement is a fundamental issue in real-world applications. Image quality metrics play a significant role in image processing. It can be used to adjust image quality and optimize parameters in many image processing applications such as enhancement, compression, restoration, etc. The image quality assessment aims to design methods for objective assessment of quality versus subjective human image quality evaluation (Wang et al, 2002). Recognition (Wang et al, 2004; Simard et al, 2002). An important objective measure was proposed in 2004 by (Wang and Bovik, 2004), where image distortion was considerd as a combination of three kinds of distortion: Correlation, luminance and contrast. Image similarity measures can be classsifed into two main directions: Statistical-based and information-theoretic based quality measures
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