MOS Prediction of SPIHT Medical Images Using Objective Quality Parameters

Correlating objective and subjective quality assessment parameters of compressed digital medical images has been an open challenging problem in tele-radiology. Establishing this correlation is crucial in determining the upper limit of image compression threshold for preserving diagnostically relevant information based on mean opinion score (MOS). This paper presents a suitable method for finding correlation between PSNR and Structural Similarity (SSIM) index objective image quality parameters with subjective MOS for SPIHT [4] compressed medical images based on six independent observers. The suggested method can be potentially used for deciding upper compression thresholds for medical images. It is found that correlation coefficient (CC) between the PSNR and MOS for CT scan and MRI images are 0.979 and 0.960 respectively whereas their corresponding values are 0.868 and 0.955 considering SSIM. Further, MOS prediction models have been proposed considering PSNR and SSIM which closely match with the subjective MOS

[1]  Jean-Bernard Martens,et al.  Quality assessment of compressed images: A comparison between two methods , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[2]  Yongmin Kim,et al.  Subjective evaluation of compressed image quality , 1992, Medical Imaging.

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

[4]  William A. Pearlman,et al.  A new, fast, and efficient image codec based on set partitioning in hierarchical trees , 1996, IEEE Trans. Circuits Syst. Video Technol..

[5]  K. Vidhya,et al.  Development of Medical Image Compression Techniques , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[6]  Jerome M. Shapiro,et al.  Embedded image coding using zerotrees of wavelet coefficients , 1993, IEEE Trans. Signal Process..

[7]  Zhou Wang,et al.  Image Quality Assessment: From Error Measurement to Structural Similarity , 2004 .

[8]  Andrew P. Bradley,et al.  Perceptual quality metrics applied to still image compression , 1998, Signal Process..

[9]  Adrian S. Lewis,et al.  Image compression using the 2-D wavelet transform , 1992, IEEE Trans. Image Process..

[10]  Michel Barlaud,et al.  Image coding using wavelet transform , 1992, IEEE Trans. Image Process..

[11]  Sugato Chakravarty,et al.  Methodology for the subjective assessment of the quality of television pictures , 1995 .

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

[13]  Jean-Bernard Martens,et al.  Quality asessment of coded images using numerical category scaling , 1995, Other Conferences.

[14]  Stefan Winkler,et al.  Issues in vision modeling for perceptual video quality assessment , 1999, Signal Process..

[15]  Pamela C. Cosman,et al.  Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy , 1994, Proc. IEEE.

[16]  V. Ravi,et al.  On-Line Evolving Fuzzy Clustering , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).