A Novel Blind Image Quality Assessment Method Based on Refined Natural Scene Statistics

Ou, Fu-Zhao; Wang, Yuan‐Gen; Zhu, Guopu

doi:10.1109/icip.2019.8803047

Cited by 59 publications

(70 citation statements)

References 15 publications

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“…The proposed algorithm—codenamed SPF-IQA—was compared to several state-of-the-art NR-IQA algorithms (BIQI [ 15 ], BLIINDS-II [ 17 ], BRISQUE [ 20 ], CORNIA [ 52 ], CurveletQA [ 26 ], DIIVINE [ 53 ], HOSA [ 54 ], FRIQUEE [ 25 ], GRAD-LOG-CP [ 23 ], IQVG [ 55 ], PIQE [ 56 ], SSEQ [ 57 ], and NBIQA [ 28 ]) whose original source codes are available online. These methods were re-trained using exactly the same database partition that was applied for the proposed method.…”

Section: Resultsmentioning

confidence: 99%

“…In Table 6 , the computational times and feature vector lengths of the learning based algorithms were compared. It can be observed that the feature extraction procedure of SPF-IQA takes less than five other state-of-the-art methods (BLIINDS-II [ 17 ], DIIVINE [ 53 ], FRIQUEE [ 25 ], IQVG [ 55 ], NBIQA [ 28 ]). The computational times were measured on a personal computer containing 8-core i7-7700K CPU in MATLAB R2019a environment.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, a softmax classifier was used for quality prediction. Ou et al [ 28 ] were the first who applied Benford’s law in image quality assessment. Namely, they pointed out features based on Benford’s law are very sensitive to white noise, Gaussian blur, and fast fading.…”

Section: Introductionmentioning

confidence: 99%

“…Namely, this is the first work that applies local fractal dimension distribution of an image for NR-IQA. Although Ou et al [ 28 ] derived first quality aware features with the help of Benford’s law, the proposed method directly measures the first digit distribution in the wavelet and spatial domain to define features. Moreover, the above mentioned novel statistical features are enriched with powerful perceptual features, such as colorfulness, global contrast factor, dark channel feature, entropy, and mean of phase congruency image.…”

Section: Introductionmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Varga

2020

J. Imaging

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“…Two well-known quantification indexes e and r [34] are used to evaluate the restoration effect of each comparison method in a real scene. We also use the NBIQA index [35] to evaluate overall image quality. Specifically, e evaluates the comparative ease of discriminating the edge between the restored image and the original blurred image.…”

Section: Quantitative Evaluationmentioning

confidence: 99%

Sand-Dust Image Restoration Based on Reversing the Blue Channel Prior

et al. 2020

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Images acquired under sand-dust weather conditions are severely degraded, with low contrast and severe color shift. The reason is that, due to the influence of sand-dust particles, light is scattered and absorbed, resulting in a blurred image and low contrast; the color shift is caused by the rapid attenuation of blue light. Therefore, to solve the problem of color shift and poor visibility in sand-dust images, this paper proposes a sand-dust image restoration method based on reversing the blue channel prior (RBCP). Under the influence of the blue channel, the dark channel prior (DCP) method will fail. Therefore, the method first reverses the blue channel of the sand-dust image and uses the dark channel prior method, which we call RBCP, and then, RBCP is used to estimate the atmospheric light and transmission map and recover the sand-dust image. The restored image shows significantly improved visibility. When estimating the transmission map, a guiding filter is used to improve the coarse transmission map, and a tolerance mechanism is introduced to modify the transmission map of bright areas in the sky to solve the problem of distortion in the sky. Finally, combined with the gray world, an adaptive color adjustment factor is introduced into the restoration model to remove the color shift. Experimental results via qualitative and quantitative evaluation demonstrate that the proposed method can effectively recover clear sand-dust images and produce results superior to those of other state-of-the-art methods.

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A Dark and Bright Channel Prior Guided Deep Network for Retinal Image Quality Assessment

Zou

Liu

2021

Pattern Recognition and Computer Vision

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A Novel Blind Image Quality Assessment Method Based on Refined Natural Scene Statistics

Cited by 59 publications

References 15 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

Sand-Dust Image Restoration Based on Reversing the Blue Channel Prior

A Dark and Bright Channel Prior Guided Deep Network for Retinal Image Quality Assessment

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