Are you sure you have to interpolate then calculate PSNR? Maybe you could add a noise to image and then calculate PSNR. According to wiki, PSNR is calculated for noisy and noise free images not interpolated ones. – Rashid Oct 23 '14 at 18:19. Peaksnr = psnr(A,ref) calculates the peak signal-to-noise ratio for the image A, with the image ref as the reference. A and ref must be of the same size and class. The PSNR block computes the peak signal-to-noise ratio, in decibels, between two images. This ratio is used as a quality measurement between the original and a compressed image. The higher the PSNR, the better the quality of the compressed, or reconstructed image.
Psnr Calculation
Evaluation of PSNR Lin Zhang, Dept. Computing, The Hong Kong Polytechnic University |
Matlab For Mac Student
Introduction
PSNR (Peak Singal-to-Noise Ratio) index is a traditional IQA metric.
Source Code
We used the PSNR implementation provided by Dr. Zhou Wang, which can be downloaded here https://ece.uwaterloo.ca/~z70wang/research/iwssim/psnr_mse.m.
Usage Notes
1. This implementation can only deal with gray-scale images. So, you need to convert the RGB image to the grayscale version, which can be accomplished by rgb2gray in Matlab.
Powerpoint text drop shadow. Evaluation Results
The results (in Matlab .mat format) are provided here. Each result file contains a n by 2 matrix, where n denotes the number of distorted images in the database. The first column is the PSNR values, and the second column is the mos/dmos values provided by the database. For example, you can use the following matlab code to calculate the SROCC and KROCC values for PSNR values obtained on the TID2008 database:
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matData = load('PSNROnTID.mat');
PSNROnTID= matData.PSNROnTID;
PSNR_TID_SROCC = corr(PSNROnTID(:,1), PSNROnTID(:,2), 'type', 'spearman');
PSNR_TID_KROCC = corr(PSNROnTID(:,1), PSNROnTID(:,2), 'type', 'kendall');
PSNROnTID= matData.PSNROnTID;
PSNR_TID_SROCC = corr(PSNROnTID(:,1), PSNROnTID(:,2), 'type', 'spearman');
PSNR_TID_KROCC = corr(PSNROnTID(:,1), PSNROnTID(:,2), 'type', 'kendall');
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The source codes to calculate the PLCC and RMSE are also provided for each database. This needs a nonlinear regression procedure which is dependant on the initialization of the parameters. We try to adjust the parameters to get a high PLCC value. For different databases, the parameter initialization may be different. The nonlinear fitting function is of the form as described in [1].
Evaluation results of PSNR on seven databases are given below. Besides, for each evaluation metric, we present its weighted-average value over all the testing datasets; and the weight for each database is set as the number of distorted images in that dataset.
Database Download earlier versions of iTunes to work with compatible operating systems and hardware. Find previous versions of iTunes. A subscription is required for the Apple Music service. 4K, 4K HDR, 4K Dolby Vision, Dolby Atmos, and HDR10 content is available on all Mac models introduced in 2018 or later with 4K-resolution screens. Until the app developer has fixed the problem, try using an older version of the app. If you need a rollback of iTunes (64-bit), check out the app's version history on Uptodown. It includes all the file versions available to download off Uptodown for that app. Download rollbacks of iTunes (64-bit) for Windows. Download iTunes 12.4.3 for Windows (64-bit - for older video cards) This iTunes installer is only for Windows 7 and later on 64 bit systems that are unable to support iTunes video playback requirements. Until the app developer has fixed the problem, try using an older version of the app. If you need a rollback of iTunes, check out the app's version history on Uptodown. It includes all the file versions available to download off Uptodown for that app. Download rollbacks of iTunes for Mac. Old versions itunes. | Results | Nonlinear fitting code | SROCC | KROCC | PLCC | RMSE |
TID2008 | PSNROnTID | NonlinearFittingTID | 0.5531 | 0.4027 | 0.5734 | 1.0994 |
CSIQ | PSNROnCSIQ | NonlinearFittingCSIQ | 0.8058 | 0.6084 | 0.8000 | 0.1575 |
LIVE | PSNROnLIVE | NonlinearFittingLIVE | 0.8756 | 0.6865 | 0.8723 | 13.3597 |
IVC | NonlinearFittingIVC | 0.6884 | 0.5218 | 0.7196 | 0.8460 | |
Toyama-MICT | 0.6132 | 0.4443 | 0.6429 | 0.9585 | ||
A57 | 0.6189 | 0.4309 | 0.7073 | 0.1737 | ||
WIQ | 0.6257 | 0.4626 | 0.7939 | 14.1381 | ||
Weighted-Average | 0.6874 | 0.5161 | 0.7020 |
Reference
[1] H.R. Sheikh, M.F. Sabir, and A.C. Bovik, 'A statistical evaluation of recent full reference image quality assessment algorithms', IEEE Trans. on Image Processing, vol. 15, no. 11, pp. 3440-3451, 2006.
Created on: May. 08, 2011
Last update: Aug. 04, 2011