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

ABSTRACT 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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    • "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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    ABSTRACT: The limitations of objective metrics such as PSNR in evaluating video quality are well known to experts but less know to general users. A video tool which exploits perceptual phenomena may report higher subjective quality but lower objective performance than a non-perceptual tool. This presents a problem when describing the performance of a perceptually motivated algorithm to general users. We propose a method for extending existing objective metrics to account for perceptual factors such as viewing distance, ambient contrast, etc. After describing the proposed algorithm extension, we examine the objective results of the proposal and perform subjective viewing tests to confirm the behavior of the extended objective metrics.
    9th Int. Workshop on Video Processing and Quality Metrics for Consumer Electronics (VPQM), Chandler, AZ; 02/2015
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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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    ABSTRACT: Butterflies can be classified by their outer morphological qualities, genital characters that can be obtained using various chemical substances and methods, which are carried out manually by preparing genital slides through some certain processes or molecular techniques. The aim of this study is to evaluate a computer vision and machine learning system that correctly identify butterfly species easier, faster and cheaper than traditional methods. In this study, human vision based image similarity methods were used as feature extractors, which were structural similarity measure (SSIM) method that depends on the combination of luminance, contrast and structural comparisons, and feature similarity index (FSIM) method that depends on combination of phase congruency and image gradient magnitude. First of all a prototype was determined for each species of 19 species, then for each butterfly, the SSIM and FSIM indexes were computed. The machine learning methods had achieved high accuracy rates for identification of butterfly species by these indexes, while it achieved 100% accuracy logistic linear classifier method The accuracy results of using SSIM and FSIM as a feature extraction method were compared with other similarity methods such as peak-signal-to-noise ratio, scale invariant feature transform, histogram comparison, image spatiogram comparison and texture methods.
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    • "For the detection of text defacement ample of work has been done using numerous text integrity techniques, whereas web image defacement is a new research field. In this work main emphasis is given on detecting website defacement in context of web images using CRC 32 checksum, hashing, and PSNR & SSIM techniques [3] [4]. "
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    ABSTRACT: Due to ad-hoc nature of web application development and design complexity of web application, it is difficult to attain fool proof web security. In recent years invaders defaced several web sites by projecting techniques such as phishing, code injection etc. In the web defacement attack the invader changes the visual appearance of the webpage. The business competitor, Insurgent and extremist groups defame the reputation of the organizations and mislead public through these types of attacks. Manual monitoring and scrutinizing these attacks on web sites is a time consuming and tedious task for law enforcement agencies. Hence there is a need to develop a system which effectively monitors the content of web sites and automatically generate alarm for any suspicious or threatening activity. In this work a prototype system is developed to scrutinize and detects the defacement activities automatically. At first phase web contents are preprocessed and stored in the web domain dictionary. Second phase checked integrity of web contents through CRC32, MD5, SHA 512, PSNR and SSIM techniques. The developed system successfully scrutinizes the web defacement attacks and it would be helpful for web administrator to monitor the web defacement attacks.
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