scholarly journals No-Reference Digital Image Quality Assessment Based on Structure Similarity

2021 ◽  
Author(s):  
Basma Ahmed ◽  
Mohamed Abdel-Nasser ◽  
Osama A. Omer ◽  
Amal Rashed ◽  
Domenec Puig
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Similarity Index ◽  
Structural Similarity ◽  
Structural Similarity Index ◽  
Spectral Kurtosis ◽  
Blurred Image ◽  
Blind Restoration ◽  
Index Measure

Blind or non-referential image quality assessment (NR-IQA) indicates the problem of evaluating the visual quality of an image without any reference, Therefore, the need to develop a new measure that does not depend on the reference pristine image. This paper presents a NR-IQA method based on restoration scheme and a structural similarity index measure (SSIM). Specifically, we use blind restoration schemes for blurred images by reblurring the blurred image and then we use it as a reference image. Finally, we use the SSIM as a full reference metric. The experiments performed on standard test images as well as medical images. The results demonstrated that our results using a structural similarity index measure are better than other methods such as spectral kurtosis-based method.

scholarly journals No-Reference Blurred Image Quality Assessment by Structural Similarity Index

2018 ◽  
Vol 8 (10) ◽  
pp. 2003 ◽  
Author(s):  
Haopeng Zhang ◽  
Bo Yuan ◽  
Bo Dong ◽  
Zhiguo Jiang
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
State Of The Art ◽  
Similarity Index ◽  
Structural Similarity ◽  
Luminance Contrast ◽  
Structural Similarity Index ◽  
Contrast Structure ◽  
Blurred Image

No-reference (NR) image quality assessment (IQA) objectively measures the image quality consistently with subjective evaluations by using only the distorted image. In this paper, we focus on the problem of NR IQA for blurred images and propose a new no-reference structural similarity (NSSIM) metric based on re-blur theory and structural similarity index (SSIM). We extract blurriness features and define image blurriness by grayscale distribution. NSSIM scores an image quality by calculating image luminance, contrast, structure and blurriness. The proposed NSSIM metric can evaluate image quality immediately without prior training or learning. Experimental results on four popular datasets show that the proposed metric outperforms SSIM and well-matched to state-of-the-art NR IQA models. Furthermore, we apply NSSIM with known IQA approaches to blurred image restoration and demonstrate that NSSIM is statistically superior to peak signal-to-noise ratio (PSNR), SSIM and consistent with the state-of-the-art NR IQA models.

scholarly journals Structural similarity index family for image quality assessment in radiological images

2017 ◽  
Vol 4 (3) ◽  
pp. 035501 ◽  
Author(s):  
Gabriel Prieto Renieblas ◽  
Agustín Turrero Nogués ◽  
Alberto Muñoz González ◽  
Nieves Gómez-Leon ◽  
Eduardo Guibelalde del Castillo
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Similarity Index ◽  
Structural Similarity ◽  
Structural Similarity Index ◽  
Radiological Images ◽  
Index Family
Sign in / Sign up

Export Citation Format

Share Document