No-Reference Quality Assessment of Deblurred Images Based on Natural Scene Statistics

Li, Leida; Yan, Ya; Lu, Zhaolin; Wu, Jinjian; Gu, Ke; Wang, Shiqi

doi:10.1109/access.2017.2661858

Cited by 43 publications

(17 citation statements)

References 48 publications

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“…Actually, compared with opinion-unaware methods, more research is based on opinion-aware methods. It is common to see methods that extract the features of NSS models [34], [35]. However, different from NSS-inspired NR stereo IQA algorithms, StereoQUE [36] built the bivariate generalized Gaussian distribution (BGGD) model in order to capture the joint statistics of the luminance and the disparity subband coefficients.…”

Section: ) No Reference Siqamentioning

confidence: 99%

No-Reference Stereo Image Quality Assessment by Learning Dictionaries and Color Visual Characteristics

Yang

Shen

et al. 2019

IEEE Access

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Since a large proportion of the information that is received daily is in the form of images, a highly effective no-reference stereo image quality assessment (SIQA) method is desired. This paper proposes an improved method that covers wide quality-aware features, including the structure, color, luminance, phase, and human visual system (HVS). To be specific, since human eyes are highly sensitive to the structure of images, the gradient magnitude (GM) and gradient orientation (GO) are extracted from the left and right views of the stereo image. Considering the influence of color distortions, the images are decomposed into the RGB channels to be processed, and the local gradient of the color image is obtained by adding up the RGB gradient vectors. In addition, according to the study of the two main visual channels, especially the cyclopean and disparity maps, the binocular-related images of position and phase congruency are generated. Correspondingly, two special dictionaries for the gradient and phase are trained to parse the high-dimensional sample sets. The experimental results show that the proposed metric always achieves high consistency with human subjective assessments for both symmetric and asymmetric distortions. INDEX TERMS Stereo image quality assessment (SIQA), no-reference, color image, gradient, phase.

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Section: ) No Reference Siqamentioning

confidence: 99%

No-Reference Stereo Image Quality Assessment by Learning Dictionaries and Color Visual Characteristics

Yang

Shen

et al. 2019

IEEE Access

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“…We further compare our proposed NRRB with two relevant quality metrics that are designed for defocus deblurred images. They are the perceptual quality evaluation for image defocus deblurring [26] and NR quality assessment of deblurred images based on natural scene statistics [27]. iv.…”

Section: Performance Evaluation On Mdd2013 Databasementioning

confidence: 99%

“…Li et al [26] presented a novel enhancement module to improve the existing NR quality metrics for defocus deblurred images, which is mainly used to quantify the texture unnaturalness in defocus deblurred images. In [27], Li et al presented an effective quality metric for defocus deblurred images. The frequency and spatial domain natural scene statistics features were first extracted to measure both the local and global aspects of distortions simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Perceptual quality evaluation for motion deblurring

Qian

2018

IET Computer Vision

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Motion deblurring has been widely studied. However, the relevant quality evaluation of motion deblurred images remains an open problem. The motion deblurred images are usually contaminated by noise, ringing and residual blur (NRRB) simultaneously. Unfortunately, most of the existing quality metrics are not designed for multiply distorted images, so they are limited in predicting the quality of motion deblurred images. In this study, the authors propose a new quality metric for motion deblurred images by measuring NRRB. For a motion deblurred image, the noise level is first estimated. Then the ringing effect is measured by incorporating visual saliency model to adapt to the characteristic of the human visual system. A reblurring-based method is proposed to extract similarity features between a motion deblurred image and its re-blurred version for evaluating the residual blur. Finally, the overall quality score of a motion deblurred image is obtained by pooling the scores of noise, ringing and blur. Experimental results conducted on a motion deblurring database demonstrate that the proposed metric significantly outperforms the existing quality metrics. In addition, the proposed NRRB metric is used for improving the existing generalpurpose no-reference metrics, and very encouraging results are achieved.

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“…Those methods are usually called distortion-specific NR IQA [3], [11]. The distortion-specific features are extracted for quality prediction in those NR IQA methods, such as the algorithm in [12] and [13] for blur, that in [14] for JPEG2000 compressed images, and that in [15] for JPEG compressed images. These methods based on distortion type only work for a certain type of distortion, and have a limitation in practice.…”

Section: Introductionmentioning

confidence: 99%

Blind Image Quality Assessment With Joint Entropy Degradation

Zhang

et al. 2019

IEEE Access

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Blind image quality assessment (BIQA) aims to evaluate the quality of an image without pristine image objectively, which is highly desired in many perception-oriented image processing systems. Distortions degrade the visual contents and cause image quality degradation. Moreover, the visual contents of an image suffer from individual degradations by different types and different levels of distortions, which makes us difficult to analyze the quality degradation. From the perspective of information theory, there is a decrease in the amount of the visual contents when images are distorted. Therefore, in this paper, the image quality is assessed through its visual entropy degradation. Researches on the neuroscience indicate that the simple cells in the local receptive field can be characterized as being spatially localized and oriented, then the local intensity, gradient, and orientation features are extracted to represent the visual contents of an image. By deducing the joint entropy equation, the joint entropy is related to the statistical distributions. Next, in order to measure the visual entropy, the joint statistical distributions of those features are calculated. Finally, by measuring the degradations on these distributions of distorted images, a novel BIQA method is proposed. The experimental results on the databases of LIVE, CSIQ, and TID2013 show that the proposed method has superior performance than other state-of-the-art BIQA methods. INDEX TERMS Blind image quality assessment (BIQA), local receptive field, joint visual entropy, joint probability distribution.

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No-Reference Quality Assessment of Deblurred Images Based on Natural Scene Statistics

Cited by 43 publications

References 48 publications

No-Reference Stereo Image Quality Assessment by Learning Dictionaries and Color Visual Characteristics

No-Reference Stereo Image Quality Assessment by Learning Dictionaries and Color Visual Characteristics

Perceptual quality evaluation for motion deblurring

Blind Image Quality Assessment With Joint Entropy Degradation

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