Mutual Information Correlation with Human Vision in Medical Image Compression

Background The lossy compression algorithm produces different results in various con-trasts areas. Low contrast area image quality declines greater than that of high contrast regions using equal compression ratio. These results were obtained in a subjective study. The objective image quali-ty metrics are more effective if the calculation method is more closely related to the human vision re-sults. Methods This study first measured the PSNR and MI for discrimination between different contrast areas responding to lossy image compression in a SMPTE electronic pattern. The MI was consistent with human vision results in SMPTE electronic phantom but PSNR was not. The measurement was also applied to compressed medical images in different contrast cropping regions. Results The MI was found to be close to human vision in CT and MR but not CRX. Both weighted PSNR and weighted MI were created to respond to the gray value and the contrast areas affected the quality estimation. Conclusion The W-PSNR and W-MI showed that they can discriminate between different contrast areas using image compression ratios and the series of lines are equal to the contrast values and better than the tranditional approach. The W-MI measures were found to perform better than W-PSNR and can be used as an image quality index.