Measure a Subjective Video Quality Via a Neural Network
We present in this paper a new method to measure the quality of the video in order to change the judgment of the human eye by an objective measure. This latter predicts the mean opinion score (MOS) and the peak signal to noise ratio (PSNR) by providing eight parameters extracted from original and coded videos. These parameters that are used are: the average of DFT differences, the standard deviation of DFT differences, the average of DCT differences, the standard deviation of DCT differences, the variance of energy of color, the luminance Y, the chrominance U and the chrominance V. The results we obtained for the correlation show a percentage of 99.58% on training sets and 96.4% on the testing sets. These results compare very favorably with the results obtained with other methods .
Keywordsvideo neural network MLP subjective quality objective quality luminance chrominance
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