Classification of image distortion based on the generalized Benford’s law

Al-Bandawi, Hussein; Deng, Guang

doi:10.1007/s11042-019-7668-3

Cited by 7 publications

(7 citation statements)

References 29 publications

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“…More specifically, Benford’s law [ 36 ] works on a distribution of numbers if that distribution spans quite a few order of magnitudes. As pointed out in [ 37 ], the first digit distribution in the transform domain of a pristine natural image harmonizes better with the Benford’s law than those of a distorted image. In this study, the normalized first digit distribution is utilized in the wavelet domain and in the image gradient domain to extract feature vectors.…”

Section: Methodsmentioning

confidence: 98%

No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features

Varga

2020

J. Imaging

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The goal of no-reference image quality assessment (NR-IQA) is to predict the quality of an image as perceived by human observers without using any pristine, reference images. In this study, an NR-IQA algorithm is proposed which is driven by a novel feature vector containing statistical and perceptual features. Different from other methods, normalized local fractal dimension distribution and normalized first digit distributions in the wavelet and spatial domains are incorporated into the statistical features. Moreover, powerful perceptual features, such as colorfulness, dark channel feature, entropy, and mean of phase congruency image, are also incorporated to the proposed model. Experimental results on five large publicly available databases (KADID-10k, ESPL-LIVE HDR, CSIQ, TID2013, and TID2008) show that the proposed method is able to outperform other state-of-the-art methods.

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Section: Methodsmentioning

confidence: 98%

No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features

Varga

2020

J. Imaging

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“…Many empirical distributions coming from natural processes follow Benford's law [26]. We hypothesize that this is also the case for noiseless MRIs.…”

Section: Methodsmentioning

confidence: 75%

“…For example, in [24], it was demonstrated that the gradient and Laplace transform magnitude adhere to Benford's law, even in medical images such as MRI [25]. Additionally, other transformations, such as the discrete cosine and wavelet transforms [26], have been found to have coefficients that conform to Benford's law. The proposed method leverages these findings to analyze the coefficients of different transforms of an image and determine the level of agreement between the expected distribution and the actual first digit distribution in the transformed domain as an indicator of the noise parameter σ.…”

Section: Introductionmentioning

confidence: 99%

Regression of the Rician Noise Level in 3D Magnetic Resonance Images from the Distribution of the First Significant Digit

Maza-Quiroga,

Thurnhofer-Hemsi,

López-Rodríguez

et al. 2023

Axioms

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This paper investigates the distribution characteristics of Fourier, discrete cosine, and discrete sine transform coefficients in T1 MRI images. This paper reveals their adherence to Benford’s law, characterized by a logarithmic distribution of first digits. The impact of Rician noise on the first digit distribution is examined, which causes deviations from the ideal distribution. A novel methodology is proposed for noise level estimation, employing metrics such as the Bhattacharyya distance, Kullback–Leibler divergence, total variation distance, Hellinger distance, and Jensen–Shannon divergence. Supervised learning techniques utilize these metrics as regressors. Evaluations on MRI scans from several datasets coming from a wide range of different acquisition devices of 1.5 T and 3 T, comprising hundreds of patients, validate the adherence of noiseless T1 MRI frequency domain coefficients to Benford’s law. Through rigorous experimentation, our methodology has demonstrated competitiveness with established noise estimation techniques, even surpassing them in numerous conducted experiments. This research empirically supports the application of Benford’s law in transforms, offering a reliable approach for noise estimation in denoising algorithms and advancing image quality assessment.

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“…Ou et al found that FDD features extracted from the discrete cosine transform (DCT) coefficients of natural images are highly sensitive to white noise, Gaussian blur, and fast fading, and applied Benford's law to the quality assessment of natural images [68]. It has been verified in [69][70][71] that the high-frequency coefficients of discrete wavelet transform (DWT), shearlet coefficients, and singular values follow the standard Benford's law. Therefore, the standard Benford's law has been applied to distortion classification [69] and natural images quality assessment [70,71].…”

Section: Image Quality Assessment Based On Benford's Lawmentioning

confidence: 99%

“…It has been verified in [69][70][71] that the high-frequency coefficients of discrete wavelet transform (DWT), shearlet coefficients, and singular values follow the standard Benford's law. Therefore, the standard Benford's law has been applied to distortion classification [69] and natural images quality assessment [70,71]. Additionally, the generalized Benford's law [72] was used to evaluate the quality of natural images with external noise such as Gaussian white noise, JPEG compression distortion, blur distortion, etc.…”

Section: Image Quality Assessment Based On Benford's Lawmentioning

confidence: 99%

A No-Reference Quality Assessment Method for Hyperspectral Sharpened Images via Benford’s Law

Hao,

Li,

et al. 2024

Remote Sensing

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In recent years, hyperspectral (HS) sharpening technology has received high attention and HS sharpened images have been widely applied. However, the quality assessment of HS sharpened images has not been well addressed and is still limited to the use of full-reference quality evaluation. In this paper, a novel no-reference quality assessment method based on Benford’s law for HS sharpened images is proposed. Without a reference image, the proposed method detects fusion distortion by performing first digit distribution on three quality perception features in HS sharpened images, using the standard Benford’s law as a benchmark. The experiment evaluates 10 HS fusion methods on three HS datasets and selects four full-reference metrics and four no-reference metrics to compare with the proposed method. The experimental results demonstrate the superior performance of the proposed method.

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Classification of image distortion based on the generalized Benford’s law

Cited by 7 publications

References 29 publications

No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features

No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features

Regression of the Rician Noise Level in 3D Magnetic Resonance Images from the Distribution of the First Significant Digit

A No-Reference Quality Assessment Method for Hyperspectral Sharpened Images via Benford’s Law

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