UNIQUE: Unsupervised Image Quality Estimation

Temel, Dogancan; Prabhushankar, Mohit; AlRegib, Ghassan

doi:10.1109/lsp.2016.2601119

Cited by 39 publications

(46 citation statements)

References 22 publications

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“…Redi et al [33] utilized the color distribution features for reduced-reference IQA algorithm. Temel et al [1] proposed an unsupervised learning approach that utilized the structural information in the YCbCr color space to improve the prediction accuracy of image quality. However, only a few BIQA algorithms concerned about the effect of color information.…”

Section: Statistics In Color Spacementioning

confidence: 99%

“…As the crucial aspect in optimization problems of image processing applications, the image quality assessment (IQA) algorithms aim to automatically and accurately evaluate the quality of a given image without accessing the ground truth [1][2][3][4][5]. Compared with full reference (FR) [6,7] IQA and reduced reference (RR) IQA [8] algorithms, blind IQA (BIQA) algorithms can estimate the perceptual quality of a distorted image without using any information of its pristine image.…”

Section: Introductionmentioning

confidence: 99%

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Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space

Tang

Jiang

et al. 2019

Applied Sciences

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To improve the evaluation accuracy of the distorted images with various distortion types, an effective blind image quality assessment (BIQA) algorithm based on the multi-window method and the HSV color space is proposed in this paper. We generate multiple normalized feature maps (NFMs) by using the multi-window method to better characterize image degradation from the receptive fields of different sizes. Specifically, the distribution statistics are first extracted from the multiple NFMs. Then, Pearson linear correlation coefficients between spatially adjacent pixels in the NFMs are utilized to quantify the structural changes of the distorted images. Weibull model is utilized to capture distribution statistics of the differential feature maps between the NFMs to more precisely describe the presence of the distortions. Moreover, the entropy and gradient statistics extracted from the HSV color space are employed as a complement to the gray-scale features. Finally, a support vector regressor is adopted to map the perceptual feature vector to image quality score. Experimental results on five benchmark databases demonstrate that the proposed algorithm achieves higher prediction accuracy and robustness against diverse synthetically and authentically distorted images than the state-of-the-art algorithms while maintaining low computational cost.

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Section: Statistics In Color Spacementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space

Tang

Jiang

et al. 2019

Applied Sciences

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“…To verify the versatility of our approach, we also compare it with some stateof-the-art NR-IQA approaches on the popular LIVE and TID2013 databases. The approaches chosen include the mainstream BLIINDS-II [13], DIIVINE [12], and BRISQUE [14], as well as the recently published IDEAL [17] and UNIQUE [48] methods. PLCC and SROCC are adopted as evaluation measures, and the experimental settings remain unchanged.…”

Section: Comparison On the Live And Tid2013 Databasesmentioning

confidence: 99%

No-reference image quality assessment for photographic images based on robust statistics

et al. 2018

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No-reference image quality assessment (NR-IQA) is developing rapidly, but there lacks of research on exploring robust statistics to improve the prediction accuracy and monotonicity of NR-IQA algorithms, in particular for assessing photographic images captured by different digital cameras where a variety of unknown distortions may happen. Hence this paper proposes a novel robuststatistics-based NR-IQA method (termed RSN) for photographic images. In RSN, we present three types of features based on robust statistics: robust natural scene statistics of multiple components, robust multi-order derivatives, and robust complementary features in the frequency domain. Then support vector regression is applied to predict image quality using the extracted features. Experimental results show that RSN remarkably outperforms state-of-the-art NR-IQA methods on the CID2013 database of photographic images, as well as on the popular LIVE and TID2013 databases.

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“…The PSNR and SSIM are full reference IQA (FR IQA) methods that do not depend upon the data. Recently, data-driven FR IQA methods based on unsupervised learning have been proposed [2], [3], which perform better than the PSNR and SSIM. However, these FR IQA methods require the original images and the distorted image to evaluate the image quality.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Convolutional Neural Network for Image Quality Assessment with Fixed Convolution Filters

Takagi

Sakurai

Hagiwara

2019

IEICE Trans. Inf. & Syst.

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Current image quality assessment (IQA) methods require the original images for evaluation. However, recently, IQA methods that use machine learning have been proposed. These methods learn the relationship between the distorted image and the image quality automatically. In this paper, we propose an IQA method based on deep learning that does not require a reference image. We show that a convolutional neural network with distortion prediction and fixed filters improves the IQA accuracy.

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UNIQUE: Unsupervised Image Quality Estimation

Cited by 39 publications

References 22 publications

Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space

Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space

No-reference image quality assessment for photographic images based on robust statistics

Discriminative Convolutional Neural Network for Image Quality Assessment with Fixed Convolution Filters

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