geozl 0.6.5

Geospatial codec extensions for OpenZL.

What is geozl?

OpenZL treats compression as a graph of codecs. Each frame carries the recipe needed to decode it, which lets a universal OpenZL decoder follow the graph without knowing how the data was originally encoded.

That model works well for one-dimensional streams, but it does not know that a raster has rows, columns, neighbours, or spatial structure. geozl adds that missing spatial layer.

A geozl codec is an OpenZL graph node that understands raster tiles. It transforms a typed numeric stream, stores the metadata needed to reverse that transform in the codec header, and lets the rest of the OpenZL graph continue as usual.

The full wire-level rules are described in SPEC.md. If you want to implement a new codec, see docs/adding-a-codec.md.

Status

geozl is experimental.

The codec set, parameters, and header layouts may change between versions. There may be no migration path for old frames if the format changes, so pin the exact geozl version for any data you cannot regenerate.

[!WARNING] geozl codecs are not part of OpenZL.

They are registered at runtime as OpenZL custom transforms and use CTids in the 0x72D700-0x72D7FF range. A frame that uses geozl codecs can only be decoded by a reader that has geozl registered. Frames that use only built-in OpenZL codecs remain portable OpenZL frames.

Install

pip install geozl

Example

geozl codecs are composed into an openzl.ext graph just like regular OpenZL nodes.

import openzl.ext as zl
import geozl

c = zl.Compressor()
g = zl.graphs.Compress()

g = zl.nodes.Zigzag()(c, g)
g = geozl.lossless.Planar(width=512)(c, g)

c.select_starting_graph(g)

Codecs

geozl currently provides two codec families:

• near-lossless codecs, under geozl.lossy
• lossless codecs, under geozl.lossless

Both families are registered as OpenZL custom codecs and can be chained with other OpenZL graph nodes.

The call column shows the Python call used to place the codec in a graph.

Near-lossless codecs

Near-lossless codecs quantize the tile once, then store enough information in the frame to report and bound the reconstruction error.

A near-lossless frame is no longer bit-exact. Instead, it declares one error mode:

• ABS: a fixed absolute tolerance, useful for elevation, depth, coordinates, and similar values.
• REL: a fixed relative tolerance, useful for radiance, reflectance, SAR amplitude, and other values where percentage error matters.

codec	call	CTid	mode	error
quant_linear	geozl.lossy.QuantLinear(max_error, dtype)	0x72D780	ABS	every value reconstructs within max_error
quant_log	geozl.lossy.QuantLog(rel_error, dtype)	0x72D781	REL	every value reconstructs within rel_error of itself

Lossless codecs

Lossless codecs are bit-exact transforms over a raster tile. After decoding, the reconstructed tile is identical to the original input.

codec	call	CTid	what it does
delta_w	geozl.lossless.DeltaW(width)	0x72D701	stores each value as a difference from its west neighbour
delta_n	geozl.lossless.DeltaN(width)	0x72D702	stores each value as a difference from its north neighbour
planar	geozl.lossless.Planar(width)	0x72D703	predicts each pixel from W + N - NW
deinterleave	geozl.lossless.Deinterleave()	0x72D704	splits an interleaved complex stream into real and imaginary lanes
med	geozl.lossless.Med(width)	0x72D705	uses the median edge detector predictor
average	geozl.lossless.Average(width)	0x72D706	predicts from the floor average of west and north neighbours
wp_static	geozl.lossless.WpStatic(width)	0x72D707	fits a static weighted predictor and stores the weights in the frame

License

BSD-3-Clause

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