Now that support for MrSID Generation 4 in third-party applications is growing, many people have asked just how we compress LiDAR data. After all, compressing raster imagery is relatively easy for us. Images are on a regular grid, so you can safely predict what the next pixel is going to hold and make some assumptions on how to most efficiently compress that information.
LiDAR data, on the other hand, is much more challenging. If you’ve ever looked at raw LiDAR returns before the data has undergone any processing, you’ll see that the returns are essentially random. Points 1, 2, and 3 can each be two inches apart from each other, while points 4 and 5 are three feet apart! As you may know, most compression routines rely on being able to find redundant information and finding a more efficient way to represent that data. The randomness of LiDAR data made compressing it a challenge for our engineers.
Luckily for us, however, randomness or predictability is not the only thing that influences compression. We’ve been using wavelet technology for years in our raster compression algorithm, and we were able to adapt that algorithm for LiDAR. By performing a transformation on the data into wavelet space, we’re able to more efficiently represent it. Furthermore, we used some more advanced techniques (like bitplane encoding and creation of a spatial index) to make the file even smaller and reduce the time it takes to view the data.
Techniques and innovations like these are what has allowed us to continue to develop advanced compression methods and provide you with the tools you need to quickly and easily do your job.
