ANALYTICAL RELATION & COMPARISON OF PSNR AND SSIM ON BABBON IMAGE AND HUMAN EYE PERCEPTION USING MATLAB

In this paper we conduct a analytical relation & comparison of PSNR and SSIM on babbon image and human eye perception using MATLAB. The measures have been categorized into pixel difference-based, and HVS-based (Human Visual System-based) measures. It has been found that measures based on HVS, on phase spectrum and on multi resolution mean square error are most discriminative to coding artifacts. In recent researches of image processing, a research has been done to measure the quality of image. The image quality assessment plays an important role where the quality is to be assessed after or before using the image for any purpose. Pixel difference-based are calculated pixel wise. As far as HVS is concerned it checks the luminosity, contrast and structure in image. Ahead in this article it will be discussed that how human visual system (HVS) is preferred over other system in an approach of full reference objective quality metrics. A comparative study between these objective quality metrics technique is always an interesting topic.

[1]  E. Peli.  Contrast in complex images. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[2]  Paul S. Fisher,et al.  Image quality measures and their performance , 1995, IEEE Trans. Commun..

[3]  Djemel Ziou,et al.  Image Quality Metrics: PSNR vs. SSIM , 2010, 2010 20th International Conference on Pattern Recognition.

[4]  Alan C. Bovik,et al.  41 OBJECTIVE VIDEO QUALITY ASSESSMENT , 2003 .

[5]  A. Bovik,et al.  OBJECTIVE VIDEO QUALITY ASSESSMENT , 2003 .

[6]  J. Kaur,et al.  Image Quality Assessment Techniques pn Spatial Domain , 2011 .

[7]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[8]  Zhou Wang,et al.  Why is image quality assessment so difficult? , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[9]  Marta Mrak,et al.  Reliability of Objective Picture Quality Measures , 2004 .

[10]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.