Full-reference and reduced-reference quality metrics based on SIFT

Farah, Joumana; Hojeij, Marie-Rita; Chrabieh, Jihad; Dufaux, Frédéric

doi:10.1109/icassp.2014.6853578

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…drawn from this population, referred to as the complete data set consisting of N observations. 11 The instrumental variable is z, the endogenous variable is x 1 and the exogenous random variables are (x −1 ; w), and where w ∈ R l is a covariate vector.…”

Section: Methodsmentioning

confidence: 99%

“…Suppose that there is a population random variable w = (z; x 1 , x −1 ; w) and that there is an independent and identically distributed sample {z i , x 1i , x −1i , w i } N i=1 drawn from this population, referred to as the complete data set consisting of N observations. 11 The instrumental variable is z, the endogenous variable is x 1 and the exogenous random variables are (x −1 ; w), and where w ∈ R l is a covariate vector.…”

Section: Methodsmentioning

confidence: 99%

“…Several image quality assessment (IQA) measures have been introduced to choose the optimal level of denoising. These approaches can be classified to full-reference (FR) in cases the original image (noise-free) is observed; reduced-reference (RR) in cases where there is a partial information about the reference; reference-free (RF) in cases where the original image is not accessible [10], [11]. As we deal with truncated distributions, the source (the complete non-truncated distribution) is intrinsically unobservable and thus, we cannot assess the success of the denoising by comparing it to the original nontruncated distribution.…”

Section: Introductionmentioning

confidence: 99%

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Semiparametric Wavelet-Based JPEG IV Estimator for Endogenously Truncated Data

Billfeld

Kim

2019

IEEE Access

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A new and an enriched JPEG algorithm is provided for identifying redundancies in a sequence of irregular noisy data points which also accommodates a reference-free criterion function. Our main contribution is by formulating analytically (instead of approximating) the inverse of the transpose of JPEGwavelet transform without involving matrices which are computationally cumbersome. The algorithm is suitable for the widely-spread situations where the original data distribution is unobservable such as in cases where there is deficient representation of the entire population in the training data (in machine learning) and thus the covariate shift assumption is violated. The proposed estimator corrects for both biases, the one generated by endogenous truncation and the one generated by endogenous covariates. Results from utilizing 2,000,000 different distribution functions verify the applicability and high accuracy of our procedure to cases in which the disturbances are neither jointly nor marginally normally distributed. 10 Unlike the practice in some other studies applying only normally distributed disturbances. 11 Capital letters indicate random variables; lower case letters indicate realizations of these random variables.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semiparametric Wavelet-Based JPEG IV Estimator for Endogenously Truncated Data

Billfeld

Kim

2019

IEEE Access

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“…However, typical applications like quality of service and quality of experience require objective measures of image quality which are well correlated with subjective perceptions [5]. The image quality assessment can be based on comparisons of distorted images with their references (full reference IQA) or on viewer impressions about distorted images without references (no‐reference IQA) [6–8]. Additionally, the practice shows that the subjective IQA also strongly depends on image content and the kind of distortions.…”

Section: Introductionmentioning

confidence: 99%

Objective estimation of subjective image quality assessment using multi‐parameter prediction

2019

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Objective evaluation of a subjective image quality assessment plays a significant role in the various image processing applications, such as compression, interpolation and noise reduction. The subjective image quality assessment does not only depend on some objective measurable artefacts, but also on image content and kind of distortions. Thus, a multi‐parameter prediction of the objective image quality assessment is proposed in this study. The prediction parameters are found minimising the mean square error related to the known subjective image quality measure (DMOS). This approach includes mostly used image quality metrics (peak signal‐to‐noise ratio, multi‐scale structural similarity image measure, feature similarity image measure, video quality measure) and two‐dimensional image quality metrics (2D IQM). The proposed multi‐parameter prediction has been verified on the test image database (LIVE) for compression, noise and blur distortions with available subjective image quality measures (DMOS). More reliable estimations are obtained using multi‐parameter prediction instead of only one measure. The best results are reached when an image content indicator is combined with the 2D IQM measure separately for different kinds of distortions.

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“…The RR IQA methods are considered practical when we can only get access to some extracted features instead of the whole original image [15]. We can use these provided features and give a reasonable estimation on the distorted image's quality [16]. In some *Correspondence: ygy@whu.edu.cn School of Electronic Information, Wuhan University, 430072 Wuhan, China practical applications, the reference image is not available to perform a comparison against.…”

Section: Introductionmentioning

confidence: 99%

RVSIM: a feature similarity method for full-reference image quality assessment

Yang

Fan

et al. 2018

J Image Video Proc.

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Image quality assessment is an important topic in the field of digital image processing. In this study, a full-reference image quality assessment method called Riesz transform and Visual contrast sensitivity-based feature SIMilarity index (RVSIM) is proposed. More precisely, a Log-Gabor filter is first used to decompose reference and distorted images, and Riesz transform is performed on the decomposed images on the basis of monogenic signal theory. Then, the monogenic signal similarity matrix is obtained by calculating the similarity of the local amplitude/phase/direction characteristics of monogenic signal. Next, we weight the summation of these characteristics with visual contrast sensitivity. Since the first-order Riesz transform cannot clearly express the corners and intersection points in the image, we calculate the gradient magnitude similarity between the reference and distorted images as a feature, which is combined with monogenic signal similarity to obtain a local quality map. Finally, we conduct the monogenic phase congruency using the Riesz transform feature matrix from the reference image and utilize it as a weighted function to derive the similarity index. Extensive experiments on five benchmark IQA databases, namely, LIVE, CSIQ, TID2008, TID2013, and Waterloo Exploration, indicate that RVSIM is a robust IQA method.

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Full-reference and reduced-reference quality metrics based on SIFT

Cited by 8 publications

References 12 publications

Semiparametric Wavelet-Based JPEG IV Estimator for Endogenously Truncated Data

Semiparametric Wavelet-Based JPEG IV Estimator for Endogenously Truncated Data

Objective estimation of subjective image quality assessment using multi‐parameter prediction

RVSIM: a feature similarity method for full-reference image quality assessment

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