New no-reference image quality assessment method based on decomposition of gradient similarity

2013 ◽  
Vol 33 (3) ◽  
pp. 691-694 ◽  
Author(s):  
Yu LIAO ◽  
Li GUO
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Reference Image ◽  
Quality Assessment Method

A WAVELET-BASED IMAGE QUALITY ASSESSMENT METHOD

2008 ◽  
Vol 06 (04) ◽  
pp. 541-551 ◽  
Author(s):  
WEN LU ◽  
XINBO GAO ◽  
DACHENG TAO ◽  
XUELONG LI
Keyword(s):  
Image Processing ◽  
Computational Complexity ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Reference Image ◽  
Subjective Perception ◽  
Good Accordance ◽  
Quality Assessment Method

Image quality is a key characteristic in image processing,10,11 image retrieval,12,13 and biometrics.14 In this paper, a novel reduced-reference image quality assessment method is proposed based on wavelet transform. By simulating the human visual system, we take the variance of the visual sensitive coefficients into account to measure a distorted image. The computational complexity of the proposed method is much lower compared with some existing methods. Experimental results demonstrate its advantages in terms of correlation coefficient, outlier ratio, transmitted information, and CPU cost. Moreover, it is also illustrated that the proposed method has a good accordance with human subjective perception.

A New Image Quality Assessment Method Based on SSIM and TV Model

2011 ◽  
Vol 65 ◽  
pp. 542-550
Author(s):  
Lu Lu Pang ◽  
Cong Li Li ◽  
De Ning Qi ◽  
Tao Zou
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Explicit Knowledge ◽  
Image Quality Assessment ◽  
Structural Similarity ◽  
Assessment Method ◽  
Reference Image ◽  
Distorted Image ◽  
Quality Assessment Method ◽  
Reference Images

In this paper, a new image quality assessment method has been proposed in which can judge the quality of images without explicit knowledge of the reference images ,it is based on the SSIM(Structural Similarity) and TV(total variation) model. Firstly, add noises to distorted image to quantitatively determine, it can get the degraded image; secondly, use the improved self-adaptive gradient weights of the TV algorithms to denoising the distorted image, it can get the “fake” reference image, then use the classical SSIM methods to make reference evaluation between the distorted image and the “fake” reference image, after modified, the results is the no reference evaluating indicator. The experiment separated use the standard testing images and the degraded images from the LIVE database to make evaluate experiment, the result show that it is consistent to the result of MOS. This method is no need of reference images, it can use widely.

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