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ZeoSync Makes Claim of Compression Breakthrough
dsb42 writes: "Reuters is reporting that ZeoSync has announced a breakthrough in data compression that allows for 100:1 lossless compression of random data. If this is true, our bandwidth problems just got a lot smaller (or our streaming video just became a lot clearer)..." This story has been submitted many times due to the astounding claims - Zeosync explicitly claims that they've superseded Claude Shannon's work. The "technical description" from their website is less than impressive. I think the odds of this being true are slim to none, but here you go, math majors and EE's - something to liven up your drab dull existence today. Update: 01/08 13:18 GMT by M : I should include a link to their press release.
They claim 100:1 compression for random data. The thing is, if thats true, then lets say we have data A size (1000)
compress(A) = B
Now, B is 1/100th the size of A, right, but it too, is random, right (size 100).
On we go:
compress(B) = C (size is now 10)
compress(C) = D (size 1).
So everything compresses into 1 byte.
Or am I missing something.
Mr Thinly Sliced
I'm going to agree with you here. If there's no pattern in the data, how can you find one and compress it. The reason things like gzip work well on c files (for instance) is because C code is far from random. How many times do you use void or int in a C file? a lot :)
:)
:)
Try compressing a wav or mpeg file with gzip. Doesn't work too well, becuase the data is "random", at least in the sense of the raw numbers. When you look at patterns that the data forms, (i.e. pictures, and relative motion) then you can "compress" that.
Here's my test for random compression
$ dd if=/dev/urandom of=random bs=1M count=10
$ du random
11M random
11M total
$ gzip -9 random
$ du random.gz
11M random.gz
11M total
$
no pattern == no compression
prove me wrong, please
My other car is first.
For example, at the top of the list Dr. Piotr Blass is listed as Chief Technical Adviser from Florida Atlantic University. But he seems to be missing from the faculty. Google doesn't turn up much on the guy either. Hmmm.
I've not even had time to check the rest yet.
Note the results are "BitPerfectTM", rather than simply saying "perfect". They try to hide it, but they are using lossy compression. That is why repeated compression makes it smaller, more loss.
"Singular-bit-variance" and "single-point-variance" mean errors.
The trick is that they aren't randomly throwing away data. They are introducing a carefully selected error to change the data to a version that happens to compress really well. If you have 3 bits, and introduce a 1 bit error in just the right spot, it will easily compress to 1 bit.
000 and 111 both happen to compress really well, so...
000: leave as is. Store it as a single zero bit
001: add error in bit 3 turns it into 000
010: add error in bit 2 turns it into 000
011: add error in bit 1 turns it into 111
100: add error in bit 1 turns it into 000
101: add error in bit 2 turns it into 111
110: add error in bit 3 turns it into 111
111: leave as it. Store it as a single one bit.
They are using some pretty hairy math for their list of strings that compress the best. The problem is that there is no easy way to find the string almost the same as your data that just happens to be really compressable. That is why they are having "temporal" problems for anything except short test cases.
Basicly it means they *might* have a breakthrough for audio/video, but it's useless for executables etc.
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- - You can't take something off the Internet! That's like trying to take pee out of a swimming pool.
1:100 average compression on all data is just impossible. And I don't mean "improbable" or "I don't belive that", it is impossible. The reason is pigeon hole principle, for simplicity assume that we are talking about 1000bit files, although you can compress some of these 1000bit files to just 10bits, you cannot possibly compress all of them to 10bits, as with 10 bits is just 1024 different configurations while 1000bits call for representations of 2 different configurations. If you can compress the first 1024, there is simply no room to represent remaining 2-1024 files.
So every loseless compression algorithm that can represent some files with other files less than original in length must expand some other files. Higher compression on some files means number of files that do not compress at all is also greater. Average compression rate other than 1 is only achiveable if there is some redundancy in original encoding. I guess you can call that redundancy "a pattern." Rar, zip, gzip etc. all achieve less than 1 compressed/original length on average because there is redundancy in originals : programs that have some instructions, prefixes with common occurance, pictures that are represented with full dword although they use a few thousand colors, sound files almost devoid of very low and very high numbers because of recording conditions etc. No compression algorithm can achive less than 1 ratio averaged over all possible strings. It is a simple consequence of pigeon hole principle and cannot be tricked.
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