Conference Paper

Image quality metrics: PSNR vs. SSIM

DOI: 10.1109/ICPR.2010.579 Conference: 20th International Conference on Pattern Recognition, ICPR 2010, Istanbul, Turkey, 23-26 August 2010
Source: DBLP


In this paper, we analyse two well-known objective image quality metrics, the peak-signal-to-noise ratio (PSNR) as well as the structural similarity index measure (SSIM), and we derive a simple mathematical relationship between them which works for various kinds of image degradations such as Gaussian blur, additive Gaussian white noise, jpeg and jpeg2000 compression. A series of tests realized on images extracted from the Kodak database gives a better understanding of the similarity and difference between the SSIM and the PSNR.

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Available from: Alain Horé, Mar 24, 2015
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    • "Objective metrics based on SSIM or MS- SSIM [5] have become popular recently, but the basic SSIM metric does not provide significantly more information than a basic PSNR calculation. For example, the work in [6] derives the relation between PSRN and SSIM. Despite the known limitations, PSNR is commonly used to evaluate system performance and application such as tuning encoding parameters due to the simplicity and application to a narrow range of parameter changes. "
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    • "Once, optimum prototypes for each species determined the similarity measures that are simpler therefore faster than image texture methods, were used to score the similarity indexes of query with each prototype. The structural similarity measure (SSIM) [14- 19], feature similarity index (FSIM) [20], peak-signal-tonoise ratio (PSNR) [19], scale invariant feature transform (SIFT) [21], histogram comparison (HISTC) [22] and image spatiogram comparison (SPATC) [23] methods were used as a similarity measure. In this study, employing the SSIM and FSIM similarity measures was assessed in detailed and the others were used for comparison. "
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    • "The peak signal-to-noise ratio (PSNR) represents the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Due to the fact that many signals have a very wide dynamic range, PSNR is often written in terms of the logarithmic decibel scale [13]. It is defined via the meansquared error (MSE) that is given by [ ] "
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