Tuesday, March 15th, 2005
Thanks to my dedicated reader, Steve, for providing feedback to my recent entries on image quality using structural similarity. He had these ideas:
Start with a low quality image (such as one that is already blurry) and degrade it more. See if results still are good — does SSIM measure this further degradation in a reasonable way?
What happens with an image that is all noise and then gets distorted? There is no structure to start with.
I ran a quick test to check out the first idea. The results follow. Click the thumbnails to view full-sized images. The image on the left is the image that has been blurred once, while the one on the right has been blurred twice.
The additional blurring operation gave a MSE = 9.9 and a MSSIM = 0.975. Qualitatively, this result makes sense — I think we lost much more visual information with the original blur than this one.
In response to the second question (what if the original image is noise only), I found that the results depend on the type of distortion. Distortion by shifting the mean or stretching the contrast gave results similar to those obtained when using natural images (MSSIM = 0.998 or so).
However, it was interesting look at the distortion caused by compressing the noise image using jpeg to achieve a MSE = 60. To achieve a MSE of 60, the jpeg algorithm couldn’t compress the noise image (shown below) very much. I can’t distinguish between the “original” and “degraded” images, therefore, my intuitive understanding is that the compressed noise-only image has a high image quality. The high MSSIM result of 0.952 coincided well with my intuition.
