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Volume 33 Issue 03
Mar.  2007
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Journal of Beijing University of Aeronautics and Astronautics  > 2007  >  33(03): 311-314.
Chang Qing, Tong Yubing, Zhang Qishan, et al. Video quality assessing model based on single image quality with different weights[J]. Journal of Beijing University of Aeronautics and Astronautics, 2007, 33(03): 311-314. (in Chinese)
Citation: Chang Qing, Tong Yubing, Zhang Qishan, et al. Video quality assessing model based on single image quality with different weights[J]. Journal of Beijing University of Aeronautics and Astronautics, 2007, 33(03): 311-314. (in Chinese)
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Video quality assessing model based on single image quality with different weights

  • 1.

    School of Electronics and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

  • 2. Institute of Intelligent Technology and System of Wuyi University, Jiangmen 529020, China

  • Received Date: 19 Apr 2006
  • Publish Date: 31 Mar 2007
    Abstract
  • Concerning HVS (human visual system) physiological characteristic with the temporal-spatio correlations of video sequence, a video quality assessing model was proposed based on single image quality with different weights. Single image quality was assessed by using NN (neural network) and SVM (support vector machine) with PSNR (peak signal to noise ratio) and SSIM (structure similarity) as two indexes describing image quality. Video quality was assessed by using the quality of each frames in the video sequence with different weights. Those weights described motion and scene changes in the video. The monotonicity of the method for images & video is 7.42% and 10.47% higher than that of PSNR and RMSE (root mean square error) is 35.90% and 10.48％ higher than that of PSNR at least. The results from simulation experiments show the model is valid.

     

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  • [1] Watson A B, Solomon J A. Model of visual contrast gain control and pattern masking [J]. Journal of Optical Society of America, 1997,14(9):2379-2391 [2] Vanden C J, Branden Lambrecht, Costantini D M, et al. Quality assessment of motion rendition in video coding [J]. IEEE Trans Circuits and Systems for Video Tech, 1999,9(5):766-782 [3] 佟雨兵,胡薇薇,杨东凯,等.视频图像质量评价综述[J].计算机辅助设计与图形学学报,2006,18(5):1-7 Tong Yubing, Hu Weiwei, Yang Dongkai, et al. Video quality assessment methods overview[J]. Journal of CAD & Computer Graphics, 2006,18(5):1-7 (in Chinese) [4] Zhou Wang, Alan C Bovik, Eero P Simoncelli. Structural approached to image quality assessment[M]. 2nd edition, In Handbook of Image and Video Processing. New York:Academic Press, 2005 [5] Vladimir N Vapnik. An overview of statistical learning theory [J]. IEEE Transactions on Neural Networks, 1999,10(5):988-999 [6] JPEG DATABASE . 2002-04-15 . http://live.ece.utexas.edu/index.htm [7] RRNR-TV Group TEST PLAN . Draft version 1.7, 2004-06-21 . http://www.vqeg.org
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