What is Lossy Compression?
- Anvita Shrivastava
- 4 days ago
- 4 min read
Heavily populated with geospatial information, GIS applications include large amounts of spatial data, including satellite images, aerial photographs, terrain models, and raster maps. Efficiently managing these large data sets is critical for performance, storage, and the transmission of data. One of the methods used to achieve this is via the process of lossy compression.
This technical guide will define how lossy compression works, the benefits and drawbacks of using it, and give a case for why you should consider using lossy compression with GIS projects.
Understanding Compression
To understand why lossy compression is needed in GIS applications, it's important first to look at data in general and to see the need for compression in GIS.
For example, satellite imagery/orthophotos/digital elevation models contain millions/billions of pixels per dataset. If we didn't use any form of compression, individual datasets can take up multiple gigabytes/terabytes, creating several problems, including:
Slow transfer time
High cost of storage
Poor performance in GIS software
Longer processing times
Compression techniques will attempt to maintain as much information as possible while reducing file size.
There are two main types of compression used in GIS applications:
Lossless compression
Lossy compression
What Is Lossy Compression in GIS?
Data compression utilizing a lossy methodology will lower the size of a file by deleting a portion of the data from the entire dataset; however, with lossless data compression, you can rebuild the original data exactly as before, whereas lossy data compression loses accuracy for smaller file sizes.
Common applications of lossy compression include raster data types below:
Orthomosaics
Web Tile Map
The objective is to have reasonable visual quality while reducing the amount of storage needed for the data.
How does Lossy Compression Work?
Lossy algorithms identify patterns contained in the pixel data associated with the raster image to remove redundant details and less distinguishable pieces.
The process starts with three major steps:
Datasets are transformed from raster format to a mathematical representation or frequency basis.
Variations in pixel intensity values that are very small are simplified to one value or incorporated into similar values.
The simplified data are then compressed for the purposes of storage.
After this three-step process, you have reduced the amount of data retained by removing detail from the raster dataset, and at the same time, you now have a file with less physical size than when it was first created.
The Benefits of Lossy Compression in GIS
When dealing with large spatial data sets, there are many practical uses of lossy compression.
Large File Size Reduction
Rasters can be compressed by 70%–95% using lossy compression. Therefore, lossy compression works very well for imaging large datasets.
Faster Data Transfer
Because smaller file sizes load faster when using web GIS or cloud-based services, lossy compression is effective in speeding up the loading of larger datasets.
More Efficient Storage
By using lossy compression, organizations are able to store a large amount of imagery without the need for large amounts of storage infrastructure.
Improved Web Mapping Service Performance
Web mapping services benefit from decreased time for tile generation and improved load time for the maps.
The Disadvantages of Lossy Compression
Although lossy compression has its advantages, there are limitations to using lossy compression that should be evaluated carefully.
Loss of Data Permanent
Once data has been altered using lossy compression, the data removed cannot be recovered.
Decreased Precision of Analysis
Some GIS analyses depend on very accurate pixel values. Using lossy compression, pixel values can become slightly distored which may affect the following types of analyses.
Spectral Analysis
Classifications
Detection of Change
Additional Data Loss due to Repeated Compression
Each time data is saved and compressed, more data may be lost, resulting in lower quality.
When Should Lossy Compression Be Used in GIS?
Lossy compression is most appropriate when visual interpretation is more important than precise pixel values.
Common scenarios include:
Web mapping services
Background basemaps
Public data portals
Visualization dashboards
Large satellite image archives
However, it should generally not be used for scientific analysis datasets where exact pixel values are required.
Feature | Lossy Compression | Lossless Compression |
Data loss | Yes | No |
File size reduction | Very high | Moderate |
Pixel accuracy | Reduced | Preserved |
Best use cases | Visualization, web maps | Scientific analysis |
Lossless formats commonly used in GIS include GeoTIFF with LZW compression, PNG, and DEFLATE compression.
Best Practices for Using Lossy Compression
GIS experts can achieve more manageable file sizes and better data quality by following these steps:
Always save a lossless or uncompressed master version of your dataset
Use lossy compression to create copies of datasets that will serve other purposes (i.e., distribution, visualization)
Avoid recompressing datasets multiple times
Choose an appropriate compression ratio for the desired image quality
Test the image quality before publishing
Modern GIS processes frequently use lossy compression to reduce costs and improve efficiency by allowing for the storage of larger raster datasets. Although the amount of data lost when compressing files with lossy compression is significant, it still achieves an acceptable level of image quality after numerous compression attempts.
Lossy compression is ideal for applications like web mapping, basemap tiles, and digital image representation. For scientific analyses and precision geospatial modeling, the use of lossless compression continues to be the option of choice.
Knowing when and how much lossy compression to apply provides GIS professionals with the opportunity to balance the quality of their data, the performance of their application, and the storage efficiency of their spatial data management strategies.
For more information or any questions regarding lossy compression, please don't hesitate to contact us at
Email: info@geowgs84.com
USA (HQ): (720) 702–4849
India: 98260-76466 - Pradeep Shrivastava
Canada: (519) 590 9999
Mexico: 55 5941 3755
UK & Spain: +44 12358 56710
Comments