Comment Structural Similarity Index Method (SSIM) (Score 2, Interesting) 291
In general your best bet would be to use an image quality metric that takes into account how the human visual system works. The 2D frequency response of the human eye looks something like a diamond, which means that we see vertical and horizontal frequencies better than diagonal ones.
In fact, most image compression techniques (including JPEG) take this into account, however, conventional ways of determining the noise in images (minimum mean squared error, peak signal to noise, root mean squares) don't factor in the human visual system.
Your best bet is to use something like the structural similarity method (SSIM) by Prof. Al Bovik of UT Austin and his student Prof. Zhou Wang (now at the University of Waterloo).
You can read all about SSIM and get example code here: http://www.ece.uwaterloo.ca/~z70wang/research/ssim/
Or read more about image quality assessment at Prof. Bovik's website: http://live.ece.utexas.edu/research/Quality/index.htm
If you don't care about how it works, and just want to use it, you can get example code for ssim in matlab at that website and C floating around the net. The method is easy to use; essentially the ssim function takes two images and returns a number between 0 and 1 that describes how similar the images are. Given two compressed images and the original image, take the SSIM between each and the original. The compressed image with the higher SSIM value is the "best".
It sounds like for your problem you might NOT have the original uncompressed image. In that case you might try checking for minimal entropy or maximum contrast in your images.
Essentially entropy would be calculated as:
h = histogram(Image);
p = h./(number of pixels in image);
entropy = -sum(p./log2(p));
You will need to make sure you scale the image appropriately and don't divide by zero! Or better yet, you should be able to find code for image entropy and contrast on the web. Just try searching for entropy.m for a matlab version.
Good luck!
In fact, most image compression techniques (including JPEG) take this into account, however, conventional ways of determining the noise in images (minimum mean squared error, peak signal to noise, root mean squares) don't factor in the human visual system.
Your best bet is to use something like the structural similarity method (SSIM) by Prof. Al Bovik of UT Austin and his student Prof. Zhou Wang (now at the University of Waterloo).
You can read all about SSIM and get example code here: http://www.ece.uwaterloo.ca/~z70wang/research/ssim/
Or read more about image quality assessment at Prof. Bovik's website: http://live.ece.utexas.edu/research/Quality/index.htm
If you don't care about how it works, and just want to use it, you can get example code for ssim in matlab at that website and C floating around the net. The method is easy to use; essentially the ssim function takes two images and returns a number between 0 and 1 that describes how similar the images are. Given two compressed images and the original image, take the SSIM between each and the original. The compressed image with the higher SSIM value is the "best".
It sounds like for your problem you might NOT have the original uncompressed image. In that case you might try checking for minimal entropy or maximum contrast in your images.
Essentially entropy would be calculated as:
h = histogram(Image);
p = h./(number of pixels in image);
entropy = -sum(p./log2(p));
You will need to make sure you scale the image appropriately and don't divide by zero! Or better yet, you should be able to find code for image entropy and contrast on the web. Just try searching for entropy.m for a matlab version.
Good luck!
