Colour Image Quality Assessment Using Structural Similarity Index and Singular Value Decomposition

2009 ◽  
pp. 55-65 ◽  
Author(s):  
Krzysztof Okarma
Keyword(s):  
Image Quality ◽  
Singular Value Decomposition ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Similarity Index ◽  
Structural Similarity ◽  
Singular Value ◽  
Structural Similarity Index ◽  
Colour Image ◽  
Value Decomposition

scholarly journals Measure of Singular Value Decomposition (M-SVD) based Quality Assessment for Medical Images with Degradation

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Ersin Elbasi
Keyword(s):  
Image Quality ◽  
Singular Value Decomposition ◽  
Mean Square Error ◽  
Similarity Index ◽  
Quality Measurement ◽  
Structural Similarity ◽  
Singular Value ◽  
Mean Square ◽  
Structural Similarity Index ◽  
Value Decomposition

We use images in several important areas such as military, health, security, and science. Images can be distorted during the capturing, recording, processing, and storing. Image quality metrics are the techniques to measure the quality and quality accuracy level of the images and videos. Most of the quality measurement algorithms does not affect by small distortions in the image. Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasonic Imaging (UI) are widely used in the health sector. Because of several reasons it might be artifacts in the medical images. Doctor decisions might be affected by these image artifacts. Image quality measurement is an important and challenging area to work on. There are several metrics that have been done in the literature such as mean square error, peak signal-noise ratio, gradient similarity measure, structural similarity index, and universal image quality. Patient information can be an embedded corner of the medical image as a watermark. Watermark can be considered one of the image distortions types. The most common objective evaluation algorithms are simple pixel based which are very unreliable, resulting in poor correlation with the human visual system. In this work, we proposed a new image quality metric which is a Measure of Singular Value Decomposition (M-SVD). Experimental results show that novel M-SVD algorithm gives very promising results against Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE), Structural Similarity Index Measures (SSIM), and 3.4. Universal Image Quality (UIQ) assessments in watermarked and distorted images such as histogram equalization, JPEG compression, Gamma Correction, Gaussian Noise, Image Denoising, and Contrast Change.

scholarly journals Image Quality Assessment Based on Quaternion Singular Value Decomposition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 75925-75935 ◽  
Author(s):  
Qingbing Sang ◽  
Yunshuo Yang ◽  
Lixiong Liu ◽  
Xiaoning Song ◽  
Xiaojun Wu
Keyword(s):  
Image Quality ◽  
Singular Value Decomposition ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Singular Value ◽  
Value Decomposition
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