A perceptual image quality index based on global and double-random window similarity

Shi, Zaifeng; Chen, Kexin; Pang, Ke; Zhang, Jiaping; Cao, Qingjie

doi:10.1016/j.dsp.2016.09.013

Cited by 12 publications

(8 citation statements)

References 24 publications

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“…Image quality evaluation based on HVS is one of the most reasonable methods when assessing pictures, because the results obtained are consistent with the subjective results [15,28]. Therefore, it is of great significance to study how the human visual system works.…”

Section: Human Visual System (Hvs)mentioning

confidence: 78%

“…However, most of these methods seem to ignore one objective fact, that the subject of evaluating mosaic image quality is the human visual system (HVS) itself [14]. HVS has been proven to evaluate the quality of mosaic images [15]. As far as we know, only Yu et al used visual saliency to describe visual attention in optimal seamline selection [1].…”

Section: Introductionmentioning

confidence: 99%

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A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention

et al. 2020

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Stitching gaps and misalignments in mosaic images can severely degrade the human visual perception of mosaic effects. Image stitching plays a key role in eliminating these unpleasant defects. In this paper, an image-stitching method for mosaic images with invisible seams is proposed, according to the research on the human visual system (HVS). By quantifying the human visual attention of images and visual discrimination about luminance difference and fine dislocations, each pixel in the stitching region is given a priority value for tracing a stitching line. Coupled with the processing of an optimal stitching line locating method and the multi-band blending algorithm, the pixels of discontinuous items in mosaic images decrease significantly and the stitching line is almost invisible. This study provides a new insight into the image-stitching field, and the experiments show that the results of the proposed method are more consistent with the human visual system in creating high-quality image mosaics.

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Section: Human Visual System (Hvs)mentioning

confidence: 78%

Section: Introductionmentioning

confidence: 99%

A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention

et al. 2020

Self Cite

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“…We evaluated the similarity between the original alpha mask and the noise alpha mask by combining gradient amplitude similarity and gradient direction similarity [42] with the Gaussian–Hermite moment. The reference image and the test image were first divided into non‐overlapping chunks [43]. The gradient magnitude and direction of the gradient were then calculated for the reference and test images, respectively.…”

Section: Proposed Methodsmentioning

confidence: 99%

Evaluating the robustness of image matting algorithm

Yuan

Fan

2020

CAAI trans. intell. technol.

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In this study, the authors propose a method to calculate the consistency of alpha masking to assess the robustness of the matting algorithm. This study evaluates consistent alpha masks based on the Gaussian–Hermite moment in combination with gradient amplitude and gradient direction. The gradient direction describes the appearance and shape of local objects in the image, and the gradient amplitude accurately reflects the contrast and texture changes of small details in the image. They selected Gaussian blur, pretzel noise, and combined noise to destroy the image, and then evaluated the consistency of the original alpha mask and noise alpha mask. To determine the robustness of the matting algorithm, they assessed the degree of consistency of the alpha mask using three different evaluation levels. The experimental results show that noise has a greater impact on the performance of the matting algorithm, which shows a decreasing trend as the noise level in the image deepens. In noisy images, the traditional matting algorithm exhibits better robustness compared to the recently proposed trap matting algorithm. Different matting algorithms present different adaptations to different noises.

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“…If an objective IQA metric can simultaneously offer higher values of CC, SROCC and KROCC, and lower values of RMSE and OR, it is deemed that this metric achieves better predictive performance. In our experiments, the predictive ability of the proposed MMVD metric is compared with eleven representative FR-IQA metrics, including two extensively used traditional metrics, i.e., PSNR and MultiScale Structural SIMilarity (MSSSIM) [61], four state-of-the-art FR-IQA metrics, i.e., VIF [5], VSNR [19], FSIM [11], NQM [20], and the latest RVSIM [12], EFS [27], GDRW [2], SLY [3], SCQI [62], which are published in 2018, 2018, 2017, 2015, 2016, respectively. Matlab source codes of these methods are downloaded from websites provided in corresponding literatures, or are derived from their authors.…”

Section: Rmse Is Computed Frommentioning

confidence: 99%

“…Meanwhile, they are relatively simple and easy to be implemented. However, they only calculate differences of pixels between the reference image and the degraded image, and don't deal with the correlation between pixels and properties of human vision, so they don't correlate well with subjective evaluations and have been widely criticized [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Image Quality Assessment by Considering Multiscale and Multidirectional Visibility Differences in Shearlet Domain

Dong

Bie

et al. 2019

IEEE Access

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Conventional objective image assessment metrics, such as mean squared error and peak signalto-noise ratio, which only calculates pixel-based differences between the original and the degraded images, are not in agreement with the human vision. In this paper, we present an improved objective full-reference image quality assessment method, called the multiscale and multidirectional visibility differences (MMVD) predictor. The proposed MMVD metric considers multiscale and multidirectional visibility differences in the domain of the discrete nonseparable shearlet transform, which emulates the multichannel structure of information processing of the human vision system. In the process of constructing the visual just noticeable difference threshold in the shearlet domain, the contrast sensitivity function and the visual masking effect which are important properties of the human visual perception are considered simultaneously to approximate the sensitivities of human visual responses. Both contrast masking and entropy masking are considered to tackle the visual masking issue. All subbands of the shearlet transform are evaluated, and perceptual errors of subbands are pooled together to yield the objective quality index of a distorted image. The extensive validation experiments are conducted on five public image databases, namely, TID2008, TID2013, CSIQ, IVC, and LIVE. The experimental results demonstrate the proposed method is well coherent with human perception and has better performance compared to the several state-of-the-art image quality metrics.INDEX TERMS Discrete nonseparable shearlet transform, image quality assessment, human visual system, visual masking, visual just noticeable difference threshold.

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A perceptual image quality index based on global and double-random window similarity

Cited by 12 publications

References 24 publications

A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention

A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention

Evaluating the robustness of image matting algorithm

Image Quality Assessment by Considering Multiscale and Multidirectional Visibility Differences in Shearlet Domain

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