Image quality assessment using the singular value decomposition theorem

Mansouri, Azadeh; Mahmoudi-Aznaveh, Ahmad; Torkamani-Azar, Farah; Jahanshahi, Javad Afshar

doi:10.1007/s10043-009-0010-y

Cited by 46 publications

(16 citation statements)

References 9 publications

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“…During the next years, several modifications of these methods were proposed by various researchers, including the Gradient SSIM [21], Quality Index based on Local Variance (QILV) [22], Riesz based Feature Similarity (RFSIM) [23] or Feature Similarity (FSIM) [24]. Some other proposed metrics utilized the Singular Value Decomposition, eg., R-SVD [25] or the information theory, eg., Information Fidelity Criterion (IFC), as well as the Visual Information Fidelity (VIF) [26], [27]. The source codes of MATLAB implementations of many metrics can be found on the webpages of their Authors and some of them were collected in the MeTriX_MuX package [28].…”

Section: Selected Full-reference Image Quality Assessment Methodsmentioning

confidence: 99%

Adaptation of Full-Reference Image Quality Assessment Methods for Automatic Visual Evaluation of the Surface Quality of 3D Prints

Okarma¹,

Fastowicz²

2019

ELEKTRON ELEKTROTECH

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Automatic visual quality assessment of the 3D printed surfaces is currently one of the most demanding challenges in additive manufacturing. Regardless of the applications of the computer vision for the 3D printing process monitoring purposes, a reliable surface quality evaluation during manufacturing may introduce brand new possibilities. The detection of some distortions and their automatic evaluation can be helpful when deciding to stop the process to save time, energy, and filament. In some cases, some further corrections can also be made for relatively small distortions. Since many general-purpose image quality assessment methods have been proposed in recent years, their applications for the quality evaluation in the additive manufacturing are investigated. As most of the metrics are full-reference and require the availability of the original perfect quality image, their direct application is not possible. Therefore, their adaptation is described in the paper together with experimental verification of classification results obtained using various metrics.

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Section: Selected Full-reference Image Quality Assessment Methodsmentioning

confidence: 99%

Adaptation of Full-Reference Image Quality Assessment Methods for Automatic Visual Evaluation of the Surface Quality of 3D Prints

Okarma¹,

Fastowicz²

2019

ELEKTRON ELEKTROTECH

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show abstract

“…The first one is the Reflection Factor (RF) [25], which utilizes not only the singular values but also right singular vector matrices to calculate the reflections, denoted as total weighted differences scaled by the singular values dependent on the energy of the image. The second algorithm, known as R-SVD [26], is based on the "referee matrix" obtained by the substitution of the reference image's left singular matrices by their equivalents calculated for the distorted one. These calculations have been made using the sliding window approach using the 8 × 8 pixels image fragments to determine the local quality.…”

Section: B Some Other Full-reference Iqa Metricsmentioning

confidence: 99%

Current Trends and Advances in Image Quality Assessment

Okarma¹

2019

ElAEE

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Image quality assessment (IQA) is one of the constantly active areas of research in computer vision. Starting from the idea of Universal Image Quality Index (UIQI), followed by well-known Structural Similarity (SSIM) and its numerous extensions and modifications, through Feature Similarity (FSIM) towards combined metrics using the multi-metric fusion approach, the development of image quality assessment is still in progress. Nevertheless, regardless of new databases and the potential use of deep learning methods, some challenges remain still up to date. Some of the IQA metrics can also be used efficiently for alternative purposes, such as texture similarity estimation, quality evaluation of 3D images and 3D printed surfaces as well as video quality assessment.

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“…To evaluate the quality of a distorted image [26,27], FR methods usually provide the most precise evaluation results in comparing with NR and RR methods. In the past three decades, many objective FR IQA methods have been put forward [3][4][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], which can be generally classified into the following three main categories:…”

Section: Introductionmentioning

confidence: 99%

“…MSVD used the mean of the differences between SVD values for assessing the image quality [17]. To enhance the performance of MSSIM and SVD, some scholars proposed improved IQA metrics by considering different regions in image are with different importance for human visual perception [18,19]. Recently, some new measures are brought out [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A Novel Image Quality Assessment Metric based on Image Gradient Vectors

Zhou¹,

Jiang²,

Wang³

et al. 2012

AISS

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Image degradation deforms the structure of image, thus analysis of structural distortions of image is beneficial to image quality assessment (IQA). This paper proposes a novel structural information-based IQA metric, in which image gradient vector, composed of magnitude and phase components, is used to describe the structural information of an image. Since the phase quantization code of the phase component can also reflect the change of image quality to some extent, Hamming distance between the phase quantization code of the reference and distorted images is defined to represent the quality map of an image for quality assessment. Because different regions in image are with different importance for human visual perception, threecomponent image model for IQA is exploited according to the gradient magnitude component, and nonuniform weights are designed to emphasize the importance of different regions in image. Experimental results of the LIVE database show that the proposed IQA metric is more consistent with subjective results, and has higher correlation coefficients compared with other classic IQA metrics.

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Image quality assessment using the singular value decomposition theorem

Cited by 46 publications

References 9 publications

Adaptation of Full-Reference Image Quality Assessment Methods for Automatic Visual Evaluation of the Surface Quality of 3D Prints

Adaptation of Full-Reference Image Quality Assessment Methods for Automatic Visual Evaluation of the Surface Quality of 3D Prints

Current Trends and Advances in Image Quality Assessment

A Novel Image Quality Assessment Metric based on Image Gradient Vectors

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