[pgpointcloud] RLE and SIGBITS heuristics

[Paul Ramsey]

Hi Oscar, 
This sounds like a slightly more sophisticated version of the work done at Natural Resources Canada for what they call “geohash tree”. They did find that they got pretty good compression (even with the simple ascii-based key!) using the scheme, and it did allow easy random access to subsets of the data.

http://2013.foss4g.org/conf/programme/presentations/60/

The downside was of course the cost of sorting things in the first place, but for a one-time cost on frequently accessed data, it’s not a bad thing. The “libght” soft dependency in pgpointcloud is to a (not so great) implementation of the scheme that I did for them a couple years ago. As a scheme, I think it cuts against the idea of having small patches that is core to the pgpointcloud concept. It makes more and more sense the larger your file is, in that it gets greater and greater leverage for random access.
ATB,
P.

-- 
Paul Ramsey
http://cleverelephant.ca
http://postgis.net

On April 17, 2015 at 11:02:47 AM, Oscar Martinez Rubi (o.martinezrubi at tudelft.nl) wrote:

Hi,

About the XYZ binding for better compression. In our research in the NL escience center and TU Delft we have been thinking (not testing yet though) about one possible approach for this.

It is based on using space filling curves. So, once you have the points that go in a block you could compute the morton/hilbert code of the XYZ. Since all the points are close together such codes will be extremely similar, so one could store only the increments which could fit in many few bits. We have not tested or compared this with any of the other compressions but we just wanted to share it with you just in case you find it useful!

An additional improvement would be to sort the points within the blocks according to the morton code. Then, when doing crop/filter operations in the blocks one can use the morton codes for the queries similarly to what we presented in our papers with the flat table (without blocks), I attach one of them (see section 5.2). In a nutshell: You convert the query region into a set of quadtree/octree nodes which can be also converted to morton code ranges (thanks to relation between morton/hilbert curve and a quadtree/octree). You scale down the ranges to increments (like you did when storing the point of the block) and then you simply do range queries in sorted data with a binary algorithm. In this way you avoid the decompression of the morton code for most of the block. This filtering is equivalent to a bbox filter so it still requires a point in polygon check for some of the points.

Kind Regards,

Oscar.


On 16-04-15 18:15, Rémi Cura wrote:
epic fail ! I had avoided html just for you

   Dataset   |subset size  | compressing   | decompressing |
             |(Million pts)|(Million pts/s)|(Million pts/s)|
Lidar        |   473.3     |    4,49       |     4,67      |
21-atributes |   105.7     |    1,11       |     2,62      |
Stereo       |    70       |    2,44       |     7,38      |

Cheers

2015-04-16 17:42 GMT+02:00 Sandro Santilli <strk at keybit.net>:
On Thu, Apr 16, 2015 at 05:30:12PM +0200, Rémi Cura wrote:
> OUps
>
> Dataset        |  subset size(Million pts) | compressing (Million pts/s) |
> decompressing (Million pts/s)
> Lidar           |            473.3                |               4,49
>               |             __4,67__
> 21 attributes |           105.7                 |
> 1,11                     |             2,62
> Stereo         |              70                  |                2,44
>                |             7,38

These tables aren't really readable here.
Could you make sure to use a fixed-width font to write those tables
and to keep lines within 70 columns at most ?

--strk;



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