Background: The lossy compression algorithm produces different results in various
contrasts 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
quality metrics are more effective if the calculation method is more closely related to the human
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 WPSNR
and can be used as an image quality index.