Looking for techniques of How to measure the Similarity/Dissimilarity between two images? I would like to compute the similarity/dissimilarity between two images L and R. 
I know one way which is : computing the histogram of blocks of each image, and then using Bhattacharyya measure I asset if the blocs are similar or not. The histogram are normalized the result are between 0 and 1. The reference to this is on this article : 

Bhattacharyya, A., “On a measure of divergence between two statistical
  populations defined by their probability distribution,” Bulletin of
  the Calcutta Mathematical Society 35, 99–110 (1943).

Well, I know that there is other measures (MI, Tanimoto, etc) and I would like to know if there is other ways to compute the Similarity/Dissimilarity between two images. I mean using histograms , it is kind of region based similarity measure, but is there some pixels related similarity techniques or more region based ones ?    
Update 1
To illustrate what I want, here is an example of two images to be compared. As you can see it is related to medical imaging (here there is two parts of the brain but I am talking about a more general case) : 
 

If only I can get a MAP of simmilarity/dissimilarity between those two images. The technique I proposed above works fine , but i am requesting some references for other similar techniques. 
 A: One particular application to medical scans, that may be relevant for your problem, is the alignment and fusion of information in images of the same tissue obtained by different methods (MRI, CT, ultrasound). This application goes by the name of image registration, and it has a very extensive literature, with a variety of software tools that you can download. 


*

*The mathematical connections are explored in ￼Mathematics
Meets Medicine: An Optimal Alignment. See also this more general review: 
Mathematical Methods In Medical Image Processing.

*Here is a comparison
of 8 different similarity measures that have been tested in this
context:

In this paper, eight intensity-based similarity measures for CT and
  ultrasound scans are evaluated. Six of these use the information from
  the histogram of images while two of them use the spatial information
  and intensity values. They are: mutual information, normalized mutual
  information, entropy correlation coefficient, joint entropy, point
  similarity measure based on mutual information, histogram energy,
  correlation ratio, and Woods criterion. Each intensity-based
  similarity measure was assessed for its capability to align and fuse
  complementary information in CT and ultrasound images. We compared its
  accuracy, capture range, distinctiveness of the optimum, risk and
  non-convergence, and number of minima.



*

*You can find algorithms for some of these similarity measures in a Matlab toolbox.
A: A quite naive method would be to create an image, where each pixel-value equals the "distance" between the corresponding pixels of the source images, i.e. $r(x,y) := \vert p(x,y)-q(x,y)\vert$ and check the size of the compressed "difference image".
The smaller the size of the compressed difference-image, the closer are the source images.  
That approach can however not unveil semantic similarity; but as it is fairly simple to implement, it should be a good start.
One could different kinds of compression (e.g. lossy and lossless) and pick the most suitable one. Simply storing the difference image in various image-file formats might already suffice.  
Edit 
seeing the medical application of similarity detection, I can imagine that the true underlying use case actually is a classification or, recognition problem.
There are other problems, that seem to be of similar nature:  


*

*Face Recognition and Optical Character Recognition; those are commonly solved successfully with Neural Networks.  

*Morphological Similarity; there a classical method is the Thompson Transform first presented in Thompson's On Growth and Form 
I found Ardeshir Goshtasby's chapter about Similarity and Dissimilarity Measures  quite useful. 
A: Have you tried G-H similarity measure? Check this paper:
http://sites.fas.harvard.edu/~cs277/papers/gromov.pdf
Memoli 2007
