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37

To select compression method you need to use a command like: gdal_translate -co "COMPRESS=method" src_dataset dst_dataset When you use compression biggest trade-off is extra processing time which is required to uncompress the image, and after uncompressing the image would still consume same amount of memory. About information loss there are two basic ...


10

With lzw and deflate compression using -co predictor=2 can help with imagery that is smoothly varying as it compresses the differences from pixel to pixel instead of the absolute values, and these will tend to be small and have more patterns (ref). Predictor is only useful with lzw and deflate compression, the option has no effect with other methods. ...


6

Try converting your rasters to numpy arrays and then check to see if they have the same shape and elements with array_equal. If they are the same, the result should be True: import arcpy, numpy raster1 = r'C:\path\to\raster.tif' raster2 = r'C:\path\to\raster.tif' r1 = arcpy.RasterToNumPyArray(raster1) r2 = arcpy.RasterToNumPyArray(raster2) d = ...


6

For big rasters GeoTiff offers the possibility to store (pre-)downscaled overviews as extra images to the GeoTiff file. This can be done with gdaladdo (= GDAL ADD Overview). When creating these overviews, you can manually tell gdal to comress them too: gdaladdo --config COMPRESS_OVERVIEW JPEG Speeds up viewing your data without adding too much size. ...


5

I suspect that there are a great number of factors that go into the choice of image format and compression scheme: Image dimensions Bit depth Image complexity (images with large areas of similar colors may actually compress better by a lossless codec than a lossy codec, and some codecs handle complex, detailed areas better than others) Multi-band support ...


4

To enable partial image decompression, simply use TILED=YES . Peter


4

You could have a try with gdalcompare.py script http://www.gdal.org/gdalcompare.html. The source code of the script is at http://trac.osgeo.org/gdal/browser/trunk/gdal/swig/python/scripts/gdalcompare.py and because it is a python script it should be easy to remove the unnecessary tests and add new ones to suit your current needs. The script seems to do pixel ...


4

You might try the USGS SRTM FTP site. I can assure you that each of the tiles you list as examples are there in their proper .hgt format and there are no suspicious files in the associated zip file. And here is the tile S35E147, buffered with the surrounding eight tiles as well:


3

I would not normally jump in when a question has effectively been answered in the comments but it seems some confusion has crept in. If you want to export your raster as is but with compression there are lots of ways of doing it but Project->Save as Image is NOT one of them. This takes a georeferenced snapshot of the current data view, which can be ...


3

Try to compress database : http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?TopicName=Compress%20File%20Geodatabase%20Data%20%28Data%20Management%29


3

My experience comparing GeoTIFF vs. Earth Resource Mapping's ECW (Enhanced Compressed Wavelet) compression is that ECW is orders of magnitude better when compressing high resolution aerial photos. Another important advantage of wavelet based compression is that, unlike older formats as GeoTIFF, JPEG - not JPEG 2000 -, just a portion of the image can be ...


2

Ultimately you'll probably need to experiment with the different options and see what meets your needs. I've been making increased use of JPEG-compressed GeoTIFFs over wavelet-based formats. My results have been pretty good. Using GDAL to do this has yielded compression ratios comparable to wavelet-based formats without too much data loss. The performance ...


2

Increasing compression above 25 often results in a severe performance degradation of the PNG encoder, thought with png8 I've observed less of it. Doing the same with JPEG results in a degradation in image quality, did not measure performance but it seemed similar by the naked eye.


2

You may even want to consider splitting your geodatabase into two parts, if possible. One for the tax maps, another for photographs. However, if your photos are attributes to your tax layer features, then your you should consider compacting and compressing your file geodatabase, as already mentioned in the comments. A couple of notes about compact vs ...


2

Compression predictors store the difference between neighbouring cell values, rather than the values themselves. If your raster has a continuously-varying value across a field, you may end up with a smaller file size when enabling a predictor. If, however, there are sudden changes in value in your raster, a predictor would probably not help much with the ...


1

The Dolphin file team? also believes that the file manager is detecting the wrong file type based on "magic numbers", see: https://forum.kde.org/viewtopic.php?f=224&t=124038&p=325861#p325861 Again, thank you looking into this and hopefully it will provide an answer for others. pitney


1

I can reproduce TeX and TGA type displays, but not the text file, in Nautilus file manager on ubuntu 12.04 LTS: SRTM3 v2.1 downloads from USGS: S35E147.hgt.zip, S36E117.hgt.zip, N33E006.hgt.zip I think there is nothing wrong with the files themselves. It's probably the way Linux is detecting file types that goes wrong in some cases here. HGT (stands for ...


1

I would suggest that you build your raster attribute table for each image, then you can compare the tables. This is not a complete check (like computing the difference between the two), but the probability that your images are different with the same histogram values is very very small. Also it gives you the number of unique values without NoData (from the ...


1

Well you are always going to lose some quality when you are compressing (unless it is lossless). With that being said, you can still compress it a fair amount and have it not be very noticeable. That is something you can play around with: quality vs. size. I've recently compressed over 1TB of imagery, and JP2 has proven to work well for me. Another option ...


1

I recommend that you review the Help page entitled Managing the performance of ArcGIS map services. In particular I think you should look at the dot point from that page below: Precompute information results when you can do so. For example, you can precompute the maps that are delivered with ArcGIS for Server and use cached map services or basemap ...


1

The most robust implementation is in the s2 geometry library with its Polygon Cover implementation. Uses S2CellIds, an improvement upon GeoHashes, as its unit.


1

As ogr2ogr apparently doesn't work on MAC OS X in this case, I install and test (indicated in GDAL: Arc/Info Binary Coverage): (-> to build the packages -> open terminal, cd to the folder and type make) 1) AVCE00 (Arc/Info (binary) Vector Coverage <-> E00 Library) avcexport' is a command-line executable that takes an Arc/Info binary coverage as ...


1

I'm guessing you're authoring this through Maestro right? I've gotten this error too. Here's how i fixed it: I dont remember why, but Gdal 1.6 didn't do it for me, i had to upgrade to the latest stable version of 1.8 (may work with 1.7, but why use older one?). You can probably compile this yourself or get FWTools, which may have the latest version of ...


1

Try and reduce the original tiff to 254dpi >if possible run the exported files through export from Global Mapper via photoshop (fireworks even better) just created and run a batch import/export web optimised .png (don't make a copy, replace the original files) The file size reduction is impressive and therefore speed up your map and tilecache on the ...



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