Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index

Xue, Wufeng; Zhang, Lei; Mou, Xuanqin; Bovik, Alan C.

doi:10.1109/tip.2013.2293423

Cited by 1,326 publications

(708 citation statements)

References 37 publications

(72 reference statements)

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“…In these cases, the assessment is usually not correlated with perceptual IQ. For the other group, there are two kinds of framework [16]. First is the bottom-up framework, which needs to simulate the processes of the HVS.…”

Section: ) Full Reference Metricsmentioning

confidence: 99%

“…Feature Similarity (FSIM) Index [25] uses phase congruence and gradient magnitude as features to characterize the image local quality. Gradient Magnitude Similarity Deviation (GMSD) [16] computes a local quality map by comparing the gradient magnitude maps of the reference and distorted image, and uses standard deviation to obtain the final IQ score. Amirshahi et al [26] proposed an IQ metric based on features extracted from Convolutional Neural Networks (CNNs), which produced good results on different databases.…”

Section: ) Full Reference Metricsmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of image quality metrics for sharpness enhancement

Cheng

Pedersen

Chen

2017

Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis

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Abstract-Image quality assessment has become a meaningful research field due to the explosive growth of image processing technologies in imaging industries. It is becoming more usual to quantify the quality of an image using image quality metrics, rather than carrying out time-consuming psychometric experiments. However, there is little research on the performance of image quality metrics on quality enhanced images. In this paper, we focus on images that have been enhanced by sharpening. A psychometric experiment was designed with observers giving scores to different images enhanced by sharpening on a display in a controlled dark environment. The results showed that full reference image quality metrics performed well when sharpening did not improve the visual image quality, while in images where sharpening increased the visual quality the performance was lower. No reference image quality metrics show better predictions than full reference image quality metrics in most cases.

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Section: ) Full Reference Metricsmentioning

confidence: 99%

Section: ) Full Reference Metricsmentioning

confidence: 99%

Evaluation of image quality metrics for sharpness enhancement

Cheng

Pedersen

Chen

2017

Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis

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show abstract

“…For performance comparison, the proposed method is compared with some representative methods including VIF [1], MAD [4], MS-SSIM [4], GSM [8], SSIM [10], NSER [14], IW-SSIM [17], FSIMc [26], NR-GLBP [36], SILD [47] and GMSD [48]. …”

Section: Database and Criteria For Evaluationmentioning

confidence: 99%

Image Quality Assessment Based on Multi-Order Local Features Description, Modeling and Quantification

Ding

Zhao

Zhi

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYImage quality assessment (IQA) plays an important role in quality monitoring, evaluation and optimization for image processing systems. However, current quality-aware feature extraction methods for IQA can hardly balance accuracy and complexity. This paper introduces multi-order local description into image quality assessment for feature extraction. The first-order structure derivative and high-order discriminative information are integrated into local pattern representation to serve as the quality-aware features. Then joint distributions of the local pattern representation are modeled by spatially enhanced histogram. Finally, the image quality degradation is estimated by quantifying the divergence between such distributions of the reference image and those of the distorted image. Experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches in consideration of not only accuracy that is consistent with human subjective evaluation, but also robustness and stability across different distortion types and various public databases. It provides a promising choice for image quality assessment development. key words: image quality assessment, image quality degradation, local pattern representation, feature extraction, visual perception

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“…The richer the texture, the larger the parameter P in Equation (1) should be. The texture information can be quantitatively measured by information entropy (Zhu et al, 2007), a number of filters with different orientations and scales (Malik et al, 2001), and by simple gradient analysis (Xue et al, 2014). Textureless areas are known to result in a low gradient and variation in color appearance, so this study uses the above two cues to quantitatively gauge the texture information, as; …”

Section: Texture-aware Semiglobal Matchingmentioning

confidence: 99%

Texture-Aware Dense Image Matching Using Ternary Census Transform

Huang¹,

Chen²,

Wu³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Textureless and geometric discontinuities are major problems in state-of-the-art dense image matching methods, as they can cause visually significant noise and the loss of sharp features. Binary census transform is one of the best matching cost methods but in textureless areas, where the intensity values are similar, it suffers from small random noises. Global optimization for disparity computation is inherently sensitive to parameter tuning in complex urban scenes, and must compromise between smoothness and discontinuities. The aim of this study is to provide a method to overcome these issues in dense image matching, by extending the industry proven Semi-Global Matching through 1) developing a ternary census transform, which takes three outputs in a single order comparison and encodes the results in two bits rather than one, and also 2) by using texture-information to self-tune the parameters, which both preserves sharp edges and enforces smoothness when necessary. Experimental results using various datasets from different platforms have shown that the visual qualities of the triangulated point clouds in urban areas can be largely improved by these proposed methods. Corresponding Author.

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Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index

Cited by 1,326 publications

References 37 publications

Evaluation of image quality metrics for sharpness enhancement

Evaluation of image quality metrics for sharpness enhancement

Image Quality Assessment Based on Multi-Order Local Features Description, Modeling and Quantification

Texture-Aware Dense Image Matching Using Ternary Census Transform

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