Comparing compression in different formats: Difference between revisions
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This is a comparison of a small selection of compression methods used in georeferenced raster imagery. The intention is to find which method gives the best "bang for buck" - the best quality for the smallest file size. The measure for quality is [https://en.wikipedia.org/wiki/Structural_similarity SSIM - structural similarity index] , which is a perception-based model to calculate the difference between two images. | This is a comparison of a small selection of compression methods used in georeferenced raster imagery. The intention is to find which method gives the best "bang for buck" - the best quality for the smallest file size. The measure for quality is [https://en.wikipedia.org/wiki/Structural_similarity SSIM - structural similarity index] , which is a perception-based model to calculate the difference between two images. | ||
We compare a lossless | == Method == | ||
We compare a lossless baseline image to a compressed image and measure the SSIM using Python package ''skimage''. This is repeated with a range of quality values for the compressed images. | |||
This should give an indication of which method is most efficient. In addition, it will indicate the quality value which will achieve the same SSIM across the compression methods. | This should give an indication of which method is most efficient. In addition, it will indicate the quality value which will achieve the same SSIM across the compression methods. | ||
Note that each of the file formats use a different scale for the input "quality" parameter. We include a wide range of the legal values from each of the formats, so this difference should not matter. | Note that each of the file formats use a different scale for the input "quality" parameter. We include a wide range of the legal values from each of the formats, so this difference should not matter. | ||
The baseline image has the following attributes: | |||
{| class="wikitable" | |||
!Attribute | |||
!Value | |||
|- | |||
| File size || 273 MB | |||
|- | |||
| Format || GeoTIFF | |||
|- | |||
| Compression || LZW | |||
|- | |||
| Dimensions || 10000 x 10000 px | |||
|- | |||
| Pixel size || 0.25 meters | |||
|} | |||
== Result == | |||
Revision as of 12:48, 21 June 2022
This is a comparison of a small selection of compression methods used in georeferenced raster imagery. The intention is to find which method gives the best "bang for buck" - the best quality for the smallest file size. The measure for quality is SSIM - structural similarity index , which is a perception-based model to calculate the difference between two images.
Method
We compare a lossless baseline image to a compressed image and measure the SSIM using Python package skimage. This is repeated with a range of quality values for the compressed images. This should give an indication of which method is most efficient. In addition, it will indicate the quality value which will achieve the same SSIM across the compression methods.
Note that each of the file formats use a different scale for the input "quality" parameter. We include a wide range of the legal values from each of the formats, so this difference should not matter.
The baseline image has the following attributes:
| Attribute | Value |
|---|---|
| File size | 273 MB |
| Format | GeoTIFF |
| Compression | LZW |
| Dimensions | 10000 x 10000 px |
| Pixel size | 0.25 meters |