Wavelet Based Sharp Features (WASH): An Image Quality Assessment Metric Based on HVS

M, Reenu; David, D. Beulah; Raj, Satyam; Nair, Madhu S.

doi:10.1109/adcons.2013.25

Cited by 19 publications

(12 citation statements)

References 6 publications

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“…Next, our RR RIQMC technique is compared with a large quantity of IQA models: 1) classical FR SSIM [6], MS-SSIM [7], VIF [9]; 2) popular FR MAD [10], IW-SSIM [8], FSIM [11], GSIM [12], IGM [13], SR-SIM [14], VSI [15], WASH [55]; 3) RR FEDM [17], RRED [18], FTQM [19], SDM [20]; and 4) NR DIIVINE [22], BLIINDS-II [23], BRISQUE [24], NFERM [25], NIQE [26], QAC [27].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement

Zhai

Wang

et al. 2016

IEEE Trans. Cybern.

347

146

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Proper contrast change can improve the perceptual quality of most images, but it has largely been overlooked in the current research of image quality assessment (IQA). To fill this void, we in this paper first report a new large dedicated contrast-changed image database (CCID2014), which includes 655 images and associated subjective ratings recorded from 22 inexperienced observers. We then present a novel reduced-reference image quality metric for contrast change (RIQMC) using phase congruency and statistics information of the image histogram. Validation of the proposed model is conducted on contrast related CCID2014, TID2008, CSIQ and TID2013 databases, and results justify the superiority and efficiency of RIQMC over a majority of classical and state-of-the-art IQA methods. Furthermore, we combine aforesaid subjective and objective assessments to derive the RIQMC based Optimal HIstogram Mapping (ROHIM) for automatic contrast enhancement, which is shown to outperform recently developed enhancement technologies.

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Section: Experimental Results and Analysismentioning

confidence: 99%

The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement

Zhai

Wang

et al. 2016

IEEE Trans. Cybern.

347

146

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“…In the CM4, first, different exponential weights are assigned to the four indicators IFC, NQM, VSNR, and VIF, and then they are multiplied to obtain the image quality score. In the CMSVR, first, four indexes, SSIM, IFC, VIF, and WASH [9] are selected as features, then these features are used to train a support vector regression (SVR) model with DMOS. Finally, the trained SVR model is used to obtain image quality scores.…”

Section: Comparison Of Subjective Consistency With Other Iqa Methods mentioning

confidence: 99%

“…Chetouani et al [8] proposed a method combining different indexes with support vector regression (CMSVR) for multiply distorted image quality assessment. In the CMSVR, first, four indexes, SSIM, IFC, VIF, and wavelet based sharp features image quality assessment metric (WASH) [9] are selected as features. Then these features are used to train a support vector regression (SVR) model with different mean opinion score (DMOS).…”

Section: Introductionmentioning

confidence: 99%

A Full-Reference Image Quality Assessment for Multiply Distorted Image based on Visual Mutual Information

Zhang¹,

Bai²,

Yan³

et al. 2019

jist

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A full-reference image quality assessment (FR-IQA) method for multi-distortion based on visual mutual information (MD-IQA) is proposed to solve the problem that the existing FR-IQA methods are mostly applicable to single-distorted images, but the assessment result for multiply distorted images is not ideal. First, the reference image and the distorted image are preprocessed by steerable pyramid decomposition and contrast sensitivity function (CSF). Next, a Gaussian scale mixture (GSM) model and an image distorted model are respectively constructed for the reference images and the distorted images. Then, visual distorted models are constructed both for the reference images and the distorted images. Finally, the mutual information between the processed reference image and the distorted image is calculated to obtain the full-reference quality assessment index for multiply distorted images. The experimental results show that the proposed method has higher accuracy and better performance for multiply distorted images.

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“…A new challenge is how to assess the quality of 3D images for various application scenarios [2][3][4][5][6][7]. Human visual system (HVS) has been seen as a vital factor to produce objective metrics for image quality assessment (IQA) [8][9][10][11]. In order to reveal perceptual characteristics of HVS, various just noticeable difference (JND) and binocular just noticeable difference (BJND) models have been proposed and employed for 2D/3D IQA [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A full‐reference stereoscopic image quality assessment index based on stable aggregation of monocular and binocular visual features

Yang

Huang

et al. 2021

IET Image Processing

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In stereoscopic image quality assessment, human visual system has been universally taken into account to detect perceptual characteristics. A novel full-reference stereoscopic image assessment metric by considering both monocular and binocular visual features of human visual system is proposed. In particular, a new region segmentation algorithm is firstly proposed to divide 3D images into occluded and non-occluded regions. The just noticeable difference model is employed on the occluded regions to formulate the monocular vision, while the binocular just noticeable difference model is applied to the non-occluded regions to reveal the binocular vision of the human visual system. In the proposed region segmentation, disparity information and Euclidean distance between stereo pairs are both adopted to solve the unstable segmentation problem of traditional methods. A new pooling strategy based on global edge features is then presented to aggregate the just noticeable difference and binocular just noticeable difference evaluation maps. In addition, some local image features as supplementary of just noticeable difference to describe visual characteristics of the human visual system are also extracted. Finally, an overall quality score is calculated based on the above-mentioned features to measure the visual quality of distorted stereo pairs. Experimental results show that the proposed metric achieves high consistency with the human visual system, and outperforms state-of-the-art algorithms on stereoscopic image quality assessment.

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Wavelet Based Sharp Features (WASH): An Image Quality Assessment Metric Based on HVS

Cited by 19 publications

References 6 publications

The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement

The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement

A Full-Reference Image Quality Assessment for Multiply Distorted Image based on Visual Mutual Information

A full‐reference stereoscopic image quality assessment index based on stable aggregation of monocular and binocular visual features

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