The world of geospatial data is vast and complex, with an ever-growing number of applications and technologies to optimize storage, transfer, and analysis. One crucial aspect of working with geospatial data is the efficient management of GeoTIFF files.
As these files can be quite large, compressing them effectively is essential for smooth workflows, quick data transfer, and accurate representation of geographical information.
In this blog post, we’ll dive deep into the world of GeoTIFF compression techniques. We’ll cover the primary purposes of compression in the context of GeoTIFFs, including reducing file size, improving transfer speed, enhancing performance, saving bandwidth, and maintaining data integrity. We’ll also explore various compression algorithms, such as None, PACKBITS, Deflate, LZW, LZMA, and ZSTD, and discuss their advantages and disadvantages.
Why compression a Geotiff?
In the context of GeoTIFFs, compression serves several important purposes:
- Reduce file size: Compression algorithms help to reduce the size of GeoTIFF files, making them more manageable and easier to store. This is particularly beneficial when dealing with large geospatial datasets that can consume significant storage space.
- Improve transfer speed: Smaller file sizes resulting from compression lead to faster data transfer speeds, which is especially important when sharing or accessing geospatial data across networks or the internet. This can improve the efficiency of workflows and reduce latency in applications.
- Enhance performance: Efficiently compressed GeoTIFFs can improve the performance of geospatial applications by reducing the time required for reading and writing data, especially when accessing data over a network or working with cloud-optimized GeoTIFFs.
- Save bandwidth: Compressed files consume less bandwidth when transferring data between systems or over the internet, making it more cost-effective and environmentally friendly.
- Maintain data integrity: Lossless compression algorithms, in particular, allow for accurate data preservation while still reducing file size. This is essential for applications that rely on the precision and integrity of geospatial data.
The purpose of compression in the context of GeoTIFFs is to strike a balance between data storage efficiency, transfer speed, and data integrity while optimizing performance for geospatial applications. Choosing the right compression algorithm and settings depends on the specific requirements of the application and the type of data being used.
Types of geotiff compression
This type of compression means that no compression is applied to the GeoTIFF file. The file size remains large, but read and write operations are faster since no time is spent compressing or decompressing the data.
PackBits is a simple, lossless compression algorithm that is suited for images with large areas of uniform color or repeating patterns. It performs run-length encoding, which means it replaces consecutive identical values with a single value and a count. It’s not as efficient as other compression methods, but it’s fast and easy to implement.
Deflate is a widely used lossless compression algorithm that combines the LZ77 algorithm and Huffman coding. It provides a good balance between compression ratio and speed. Deflate can be customized with different compression levels and predictor settings to optimize performance for specific data types.
LZW is another lossless compression algorithm that works well on data with repeating patterns. It’s based on a dictionary method, building a table of repeated patterns as the data is processed. LZW typically provides better compression ratios than PACKBITS but may be slower in some cases. It’s widely used in the geospatial industry due to its effectiveness and compatibility.
LZMA (Lempel-Ziv-Markov chain Algorithm):
LZMA is a lossless compression algorithm that provides high compression ratios at the cost of slower compression and decompression times. It uses a combination of LZ77 and range coding, with a larger dictionary size than LZW or Deflate. LZMA is more suitable for situations where file size is a priority, and processing time is less critical.
Zstandard is a modern, lossless compression algorithm developed by Facebook. It offers a balance between high compression ratios and fast compression/decompression speeds. ZSTD supports different compression levels and predictor settings, allowing for customization based on specific data types and requirements. It’s a popular choice for geospatial data compression due to its efficiency and performance.
Table describing the difference between these types of geotiff compression
|No compression is applied to the GeoTIFF file.
|Fast read/write times
|Large file size
|Simple, lossless run-length encoding compression algorithm.
|Fast, suitable for simple data patterns
|Lower compression ratios than other methods
|Lossless compression combining LZ77 and Huffman coding.
|Good compression ratio, customizable
|Slower than PACKBITS or LZW
|Lossless dictionary-based compression algorithm.
|Better compression ratio than PACKBITS
|May be slower than PACKBITS
|High compression ratio lossless algorithm using LZ77 and range coding.
|High compression ratios
|Slower compression/decompression times
|Modern lossless compression algorithm developed by Facebook.
|High compression ratio, fast, customizable
|May require newer software/libraries
Difference between lossless and lossy compression
In the context of GeoTIFFs, lossless and lossy refer to the different ways data can be compressed within the file format.
- The original data is preserved exactly, without any loss of information when the file is compressed and later decompressed.
- Lossless compression algorithms include PACKBITS, Deflate, LZW, LZMA, and ZSTD.
- The file size reduction might be smaller compared to lossy compression, but the data integrity is maintained.
- Suitable for applications where maintaining the accuracy and precision of geospatial data is critical.
- The compression process reduces file size at the expense of some loss of information or precision in the original data.
- The decompressed data is an approximation of the original data and may contain some minor errors or differences.
- In the context of GeoTIFFs, Limited Error Raster Compression (LERC) is an example of a lossy compression method. It can be used standalone or in combination with other compression methods (e.g., LERC_ZSTD, LERC_DEFLATE).
- Lossy compression can provide significantly higher compression ratios compared to lossless methods but at the cost of data precision.
- Suitable for applications where a reduction in precision is acceptable, and smaller file sizes or faster data transfer is more important.
When choosing a compression method for GeoTIFFs, it is essential to consider the specific requirements of your application, such as the importance of data integrity and precision, the acceptable level of loss, and the need for efficient storage and data transfer.