A new psychovisual paradigm for image quality assessment: from differentiating distortion types to discriminating quality conditions

Gu, Ke; Zhai, Guangtao; Yang, Xiaokang; Zhang, Wenjun

doi:10.1007/s11760-013-0445-2

Cited by 18 publications

(7 citation statements)

References 20 publications

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“…SROCC is used in this paper because it is a significant performance index and has been widely used in quite a few methods for parameter tuning [3], [57]- [58]. Fig.…”

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.

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344

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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“…SROCC is used in this paper because it is a significant performance index and has been widely used in quite a few methods for parameter tuning [3], [57]- [58]. Fig.…”

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.

Self Cite

344

146

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“…First, the design of our technique benefits from some principles. Image structure has an extremely important influence on the visual quality of human judgement, and it has been widely used in most existing IQA models [16][17][18][19][20][21][22][23][24][25][26][27][28] and some related applications [7][8][9][10][11][12][13]. In [29], the authors have pointed out that using a proper Gaussian kernel to filter the input original and distorted images can effectively extract image structure.…”

Section: Resultsmentioning

confidence: 99%

“…Nonetheless, MSE was found to poorly correlate with human judgements of image quality [15]. The epoch-making structural similarity index (SSIM) [16] was developed in light of luminance, contrast and structural similarities between the original and distorted image signals, and its variants [17][18][19][20][21][22] have been designed to pursue higher accuracy since then. Other advanced FR IQA methods [23][24][25][26][27][28][29] were explored via the natural scene statistics (NSS) [30], low-level vision [31], and free energy principle [32].…”

Section: Introductionmentioning

confidence: 99%

Content-weighted mean-squared error for quality assessment of compressed images

Wang

Zhai

et al. 2015

SIViP

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Image quality assessment (IQA) has been intensively studied, especially for the full-reference (FR) scenario. However, only the mean-squared error (MSE) is widely employed in compression. Why other IQA metrics work ineffectively? We first sum up three main limitations including the computational time, portability, and working manner.To address these problems, we then in this paper propose a new content-weighted MSE (CW-MSE) method to assess the quality of compressed images. The design principle of our model is to use adaptive Gaussian convolution to estimate the influence of image content in a block-based manner, thereby to approximate the human visual perception to image quality. Results of experiments on six popular subjective image quality databases (including LIVE, TID2008, CSIQ, IVC, Toyama and TID2013) confirm the superiority of our CW-MSE over state-of-the-art FR IQA approaches.

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“…Another issue overlooked by most existing IQA methods is that the relationship between metrics and the empirical DMOS scale is strongly non-linear [17], [28]. As a matter of fact, the Spearman Rank Order Correlation Coefficient (SROCC) [29], often employed to compare the performance of different metrics, is insensitive to linearity issues.…”

Section: A Shortcomings Of Existing Methodsmentioning

confidence: 99%

A Detail-Based Method for Linear Full Reference Image Quality Prediction

Claudio

Jacovitti

2018

IEEE Trans. on Image Process.

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Abstract-In this paper, a novel Full Reference method is proposed for image quality assessment, using the combination of two separate metrics to measure the perceptually distinct impact of detail losses and of spurious details. To this purpose, the gradient of the impaired image is locally decomposed as a predicted version of the original gradient, plus a gradient residual. It is assumed that the detail attenuation identifies the detail loss, whereas the gradient residuals describe the spurious details. It turns out that the perceptual impact of detail losses is roughly linear with the loss of the positional Fisher information, while the perceptual impact of the spurious details is roughly proportional to a logarithmic measure of the signal to residual ratio. The affine combination of these two metrics forms a new index strongly correlated with the empirical Differential Mean Opinion Score (DMOS) for a significant class of image impairments, as verified for three independent popular databases. The method allowed alignment and merging of DMOS data coming from these different databases to a common DMOS scale by affine transformations. Unexpectedly, the DMOS scale setting is possible by the analysis of a single image affected by additive noise.

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A new psychovisual paradigm for image quality assessment: from differentiating distortion types to discriminating quality conditions

Cited by 18 publications

References 20 publications

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

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

Content-weighted mean-squared error for quality assessment of compressed images

A Detail-Based Method for Linear Full Reference Image Quality Prediction

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