- Psnr Calculation
- Psnr Calculation In Matlab For Mac Free
- Psnr Calculation In Matlab For Mac Pdf
- Psnr Calculation In Matlab For Mac Pro
The following Matlab project contains the source code and Matlab examples used for psnr calculator. A simple PSNR calculator that might be handy in measuring the PSNR between two frames in a video. Typically used in measuring the distortion when videos are encoded and decoded. Peak-Signal to Noise Ratio (PSNR) 1. The PSNR is most commonly used as a measure of quality of reconstruction of lossy compression codec’s (e.g., for image compression). 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.
Evaluation of PSNR Lin Zhang, Dept. Computing, The Hong Kong Polytechnic University |
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.
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.
Psnr Calculation
Database | 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
Psnr Calculation In Matlab For Mac Free
PSNR is used to measure the quality of reconstruction of lossy and lossless compression (e.g., for image compression). The signal in this case is the original data, and the noise is the error introduced by compression. When comparing compression codecs , PSNR is an approximation to human perception of reconstruction quality. Although a higher PSNR generally indicates that the reconstruction is of higher quality, in some cases it may not. PSNR is most easilydefined via the mean squared error.
Psnr Calculation In Matlab For Mac Pdf
Here, MAXI is the maximum possible pixel value of the image. When the pixels are represented using 8 bits per sample, this is 255. More generally, when samples are represented using linear PCM with B bits per sample, MAXI is 2B−1. For color images with three RGB values per pixel, the definition of PSNR is the same except the MSE is the sum over all squared value differences divided by image size and by three.