AC (and now Finite State Entropy https://github.com/Cyan4973/FiniteStateEntropy) are really good, but carry the weight of spending more time than we had to do the task. Goulomb coding the distance between high bits was a great, simple to implement solution for us. Another issue was the compression time. It needed to be quick, and AC is not known to be fast.

In future, I hope to use FSE for almost all my lossless coders.


On 8 March 2014 15:13, Colt McAnlis <mainroach@gmail.com> wrote:
Why not just use industry standard Arithmetic Compression?

If you calculate the estimated entropy for that data set (http://planetcalc.com/2476/) you end up with about H=0.06 for 7k set bits in a 1024*1024 stream, and H=0.01 for 1k set bits.
That being entropy (or minimum bits per symbol) should yield 70kb and 10k after compression, respectively. Or, with AC, and those sparse values, you can get around 94% compression and 99% compression. (if my early morning math is right)

AC is a known algorithm, easy examples to find on the web. And as far a serialization is concerned, it's pretty straight forward to just encode your bits, then decode; no extra special data structures needed.

~Main


On Fri, Jan 31, 2014 at 3:27 PM, Richard Fabian <raspo1@gmail.com> wrote:
there was a lot of similarity, but with no processng at the destination, we decided that the Golomb-rice algorithm was good enough. Dropped our data by 70%. That was enough of a saving for this one area.


On 31 January 2014 21:32, Jon Watte <jwatte@gmail.com> wrote:
Encode a single starting value at full bandwidth then send a stream of differences:

Which is equivalent to a wavelet "lift" transform :-)

Back to the original question: If you have a previous save game, is there lots of similarity in the next save? If so, can you save a delta instead?
If not, what solution did you end up choosing?

Sincerely,

jw






Sincerely,

Jon Watte


--
"I find that the harder I work, the more luck I seem to have." -- Thomas Jefferson


On Tue, Jan 21, 2014 at 12:08 AM, Robin Green <robin.green@gmail.com> wrote:

Here's a modern Run Length version of Golomb-Rice encoding designed for compressing general data with a Generalized Gaussian distribution. Works best if you can prove there are, in most cases, small differences between adjacent data (but handily you get to define what "adjacent" means to your stream). Encode a single starting value at full bandwidth then send a stream of differences:


As a bonus, if you're encoding depth images, you can remap the values to leave zero as an out-of-band value:


Yes, I know that wasn't the question you asked but at least it's an algorithm. :-)

- Robin Green



On Fri, Jan 17, 2014 at 10:04 AM, Alex Walters <ajw.walters@gmail.com> wrote:
Its on the fringe of being useful, but one thing you could look at to reduce your data size is Exponential Golomb coding (http://en.wikipedia.org/wiki/Exponential-Golomb_coding), its used in H.264 entropy encoding among other things.

Instead of writing a full n-bit values for every number you store, it produces a bit stream of codes, using far less bits for small numbers - take a look at the wiki page, its pretty simple to see whats going on when you look at the example. It can reduce the amount of data you have to move, and from what I remember from when I worked with video, it produces much more predictable (and compressible) stream of bits for the next compression scheme along (variable length encoding, or arithmetic encoding in the case of H.264) - I've not looked into the details but could probably improve the compression of the stream you get through gzip.

Regards,

Alex



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