A Perceptual Image Quality Assessment Metric Using Singular Value Decomposition

Wang, Shuigen; Cui, Dongshun; Wang, Baoxian; Zhao, Baojun; Yang, Jinglin

doi:10.1007/s00034-014-9840-3

Cited by 8 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The formula for calculating structural similarity is as follows: where is the average value of the spectral pixel decomposition model fraction; is the average value of the visual interpretation fraction of Landsat 7; and are the variance between the modeled fraction and the visual interpretation fraction, respectively; is the covariance; and and are constants. The closer the SSIM value is to 1, the higher the similarity between the modeled fraction and the visual interpretation fraction ( Dosselmann & Yang, 2011 ; Wang et al, 2014 ).…”

Section: Methodsmentioning

confidence: 99%

Examining long-term natural vegetation dynamics in the Aral Sea Basin applying the linear spectral mixture model

Wang

Zhao

et al. 2021

PeerJ

View full text Add to dashboard Cite

Background Associated with the significant decrease in water resources, natural vegetation degradation has also led to many widespread environmental problems in the Aral Sea Basin. However, few studies have examined long-term vegetation dynamics in the Aral Sea Basin or distinguished between natural vegetation and cultivated land when calculating the fractional vegetation cover. Methods Based on the multi-temporal Moderate Resolution Imaging Spectroradiometer, this study examined the natural vegetation coverage by introducing the Linear Spectral Mixture Model to the Google Earth Engine platform, which greatly reduces the experimental time. Further, trend line analysis, Sen trend analysis, and Mann–Kendall trend test methods were employed to explore the characteristics of natural vegetation cover change in the Aral Sea Basin from 2000 to 2018. Results Analyses of the results suggest three major conclusions. First, the development of irrigated agriculture in the desert area is the main reason for the decrease in downstream water. Second, with the reduction of water, the natural vegetation coverage in the Aral Sea Basin showed an upward trend of 17.77% from 2000 to 2018. Finally, the main driving factor of vegetation cover changes in the Aral Sea Basin is the migration of cultivated land to the desert.

show abstract

Section: Methodsmentioning

confidence: 99%

Examining long-term natural vegetation dynamics in the Aral Sea Basin applying the linear spectral mixture model

Wang

Zhao

et al. 2021

PeerJ

View full text Add to dashboard Cite

show abstract

“…SVD is a significant matrix decomposition method that can extract the main components that represent the useful signal from unknown small stationary or non-stationary signal components, which makes it more successful than other methods for image decomposition [13], dictionary learning [14], and de-noising of electronic noise data [15]. SVD has good stability in noise reduction.…”

Section: Introductionmentioning

confidence: 99%

Development of a Method for Selection of Effective Singular Values in Bearing Fault Signal De-Noising

Gao

Wang

et al. 2016

Applied Sciences

View full text Add to dashboard Cite

Singular value decomposition (SVD) is a widely used and powerful tool for signal extraction under noise. Noise attenuation relies on the selection of the effective singular value because these values are significant features of the useful signal. Traditional methods of selecting effective singular values (or selecting the useful components to rebuild the faulty signal) consist of seeking the maximum peak of the differential spectrum of singular values. However, owing to the small number of selected effective singular values, these methods lead to excessive de-noised effects. In order to get a more appropriate number of effective singular values, which preserves the components of the original signal as much as possible, this paper used a difference curvature spectrum of incremental singular entropy to determine the number of effective singular values. Then the position was found where the difference of two peaks in the spectrum declines in an infinitely large degree for the first time, and this position was regarded as the boundary of singular values between noise and a useful signal. The experimental results showed that the modified methods could accurately extract the non-stationary bearing faulty signal under real background noise.

show abstract

Separation of Single Frequency Component Using Singular Value Decomposition

Zhao

Bang-yan

2018

Circuits Syst Signal Process

View full text Add to dashboard Cite

A Perceptual Image Quality Assessment Metric Using Singular Value Decomposition

Cited by 8 publications

References 11 publications

Examining long-term natural vegetation dynamics in the Aral Sea Basin applying the linear spectral mixture model

Examining long-term natural vegetation dynamics in the Aral Sea Basin applying the linear spectral mixture model

Development of a Method for Selection of Effective Singular Values in Bearing Fault Signal De-Noising

Separation of Single Frequency Component Using Singular Value Decomposition

Contact Info

Product

Resources

About