(36 of the 64 values are considered to be "lower diagonal" in the bottom right area) The actual number :
avg : 3.18 : 0 : 37.01 1 : 16.35 2 : 9.37 3 : 6.48 4 : 5.04 5 : 3.88 6 : 3.22 7 : 3.02 8 : 2.49 9 : 2.34 10 : 2.09 ...The interesting thing about this is that it has a very flat tail, much flatter than you might expect. For example, if the probability of a given coefficient being zero or nonzero was an indepedent random event, the distribution would be binomial; it peaks flatter and is then much faster to zero :
avg : 3.18 : 0 : 13.867587 1 : 28.162718 2 : 27.802496 3 : 17.775123 4 : 8.272518 5 : 2.986681 6 : 0.870503 7 : 0.210458 8 : 0.043037 9 : 0.007553 10 : 0.001150 ...What this tells us is the probability of a given coefficient being zero is highly *not* idependent. They are strongly correlated - the more values that are on, the more likely it is that the next will be on. In fact we see that if there are 6 values on, it's almost equally likely there are 7, etc. , that is : P(n)/P(n-1) goes toward 1.0 as n gets larger.
Also, amusingly the first two ratios P(1)/P(0) and P(2)/P(1) are both very close to 0.5 in every image I'm tried (in 0.4 to 0.6 generally). What this means is it wouldn't be too awful just to code the # of values on with unary, at least for the first bit (you could use something like an Elias Gamma code which uses unary at first then adds more raw bits).
Now for pretty pictures. Everyone has seen graphics like this, showing the L2 energy of each coefficient in the DCT : (none of these pictures include the DC because it's weird and different)
This shows the percentage of the time the value is exactly zero :
Now for some more interesting stuff. This shows the percent of correlation to the block above the current one : (to the north) :
Note in particular the strong correlation of the first row.
The next one is the correlation to the block to the left (west) :
Finally the fun one. This one shows the correlation of each coefficient to the other 63 coefficients in the same block :
The self-correlation is 100% which makes it a white pixel obviously. Black means 0% correlation. This is absolute-value correlation in all cases (no signs). There are a lot of patterns that should be pretty obvious to the eye. Beware a bit in over-modeling on these patterns because they do change a bit from image to image, but the general trend stays the same.
And another one from a different image :
This one's from Lena. A few things I think are particularly interesting - in the upper left area, which is where most of the important energy is, the correlation is most strong diagonally. That is, you see these "X" shape patterns where the center pixel is correlated mainly to it's diagonal neighbors, not the one's directly adjacent to it.