Is ratio of set and reset bits an indicator of compressibility?

 I'm working on a and exploring a binary transform and am forming an 
opinion on what qualities are important to test for.

 So far I believe that a ratio between set and reset bits is a general 
indicator of compressibility. This I have understood from reading this 
group.

 Is this a correct notion? 

 I'm generating files of length 415241 bytes and testing for ratios of 
set and reset.  Currently saving each file generating a larger ratio then 
the last. in the neighborhood of 8000 bits difference at the moment.

Thanks for the "Lovin' Spoonful."http://www.youtube.com/watch?
v=zWXcjYNZais



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On Mar 17, 2:07=A0pm, Ernst Berg <Ernst_B...@sbcglobal.net> wrote:
> Is ratio of set and reset bits an indicator of compressibility?
>
> =A0I'm working on a and exploring a binary transform and am forming an
> opinion on what qualities are important to test for.
>
> =A0So far I believe that a ratio between set and reset bits is a general
> indicator of compressibility. This I have understood from reading this
> group.
>
> =A0Is this a correct notion?
>
> =A0I'm generating files of length 415241 bytes and testing for ratios of
> set and reset. =A0Currently saving each file generating a larger ratio th=
en
> the last. in the neighborhood of 8000 bits difference at the moment.


It is, as it implies a bias in the input (and clearly would allow an
arithmatic compressor with two symbols to compress the file), but it's
very weak.  Consider, for example, a file full of uppercase Z's: the
number of zero and one bits will be identical, which would imply low
compressibility, while it's hard to imagine a file that would compress
better.

BTW, you're looking for the general concept of entropy.  See:

http://en.wikipedia.org/wiki/Entropy_(information_theory)

On 17.03.2011 20:07, Ernst Berg wrote:
>
> Is ratio of set and reset bits an indicator of compressibility?

Partially. Having a bias of 0 in respect to 1's indicates 
compressibility, i.e. a non-maximal zero-entropy. But the reverse does 
not hold, i.e. just because a source has maximal zero-order entropy does 
not mean it is not compressible. It could have non-maximal first-order 
entropy. Consider a bit sequence generated by a Bernoulli source, and 
transform it by duplicating each bit in the input sequence, gaining an 
output twice as long. Clearly, the ratio of 1 to 0 should be 
approximately 50/50, but yet this expanded sequence is compressible.

The proper notion for "compressibility" (in terms of the Shannon 
theorem) is to consider blocks of length m, compute the entropy of these 
blocks divided by the block length, and then make the blocks larger and 
larger. If *that* isn't non-maximal, the source is compressible - if and 
only if. This is Shannon's lossless source coding theorem.

In practical terms, you cannot, of course, make the block size 
arbitrarily large, and still get some *useful* statistical information 
from it.

Greetings,
	Thomas

On Thu, 17 Mar 2011 21:59:29 -0700, robertwessel2@yahoo.com wrote:

Thank you.

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On Fri, 18 Mar 2011 10:12:40 +0100, Thomas Richter wrote:

Thank you.

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