Understanding the Target Compression Ratio when using the ERDAS ECW JPEG2000 SDK

It is important to note that when using the ERDAS ECW JPEG2000 SDK, the ECW and JPEG2000 target compression ratio makes no guarantees about the actual output size that will be achieved. Images with certain features (for example, air photos showing large regions of a similar color, like background values, oceans or forests) are easier to compress than others (completely random images).

When compressing images there is a tradeoff between the degree of compression achieved and the quality of the resulting image. The highest rates of compression can only be achieved by discarding some less important data from the image, known as lossy decompression. In the ERDAS ECW JPEG2000 SDK, the target compression ratio is an abstract figure representing your choice in this tradeoff. It approximates the likely ratio of input file size to output file size, given certain parameters for compression.

It is often unwise to try and force each image into a compressed file of the same size. This is because the resulting compressed images will have widely varying quality levels which may reduce their usefulness and any subsequent processing will have decreased value when combining different image tiles of different quality compression together.

Below is a table taken from the ERDAS IMAGINE 2010 Help (to be released later in 2011). This table was created from in-house testing and customer feedback.

Imagery	Target Compression Ratio
Visually Lossless RGB Image	Crisp Image Interpretation (2x zoom)	4:1
Near Visually Lossless RGB Image	Clear Image Interpretation (2x zoom)	6:1
RGB Image	High Quality Printed Maps and typical GIS applications	15:1 to 25:1
RGB Image	Internet or Email Distribution	15:1 to 40:1
Visually Lossless Panchromatic Image	Crisp Image Interpretation (2x zoom)	2:1
Near Visually Lossless Panchromatic Image	Clear Image Interpretation (2x zoom)	3:1
Panchromatic Image	High Quality Printed Maps and typical GIS applications	10:1 to 15:1
Panchromatic Image	Internet or Email Distribution	15:1 to 30:1
Visually Lossless Multispectral Image	Crisp Image Interpretation (2x zoom)	3:1
Near Visually Lossless Multispectral Image	Clear Image Interpretation (2x zoom)	4:1
Numerically Lossless RGB, Panchromatic, and Multispectral (JPEG 2000 Only)	Imagery with perfect numeric reconstruction	1:1

Numerically Lossless or Visually Lossless Wavelet Compression

I hear so very often, we need lossless image compression (meaning numerically lossless - NL). I respect the point much more when I hear it from remote sensing and photogrammetry scientists than from GIS users.

Why my differentiation? Am I diss’ing the GIS folks? I hope not, that is not my intention.

Way back in 2001 I did a little test. I was at Georgia Tech Center for GIS (CGIS) and was asked by the State of Georgia to determine what compression level was acceptable to compress the state’s Color-Infrared aerials. Wavelet NL was not available through COTS software at the time (MrSID and ECW) and CGIS was not funded to research a new compression method. We were told to research what COTS compression level to use.

So, we pulled together engineering students, business students, architectural students, geospatial scientists, and secretaries. Most had some geospatial training and a few did not. We displayed an 8-bit uncompressed image, and compressed versions of the same image at 10:1, 15:1, 20:1, 25:1 and 30:1 compressions.

The people were allowed display images side-by-side, use swipe, blend, and fade functions. They could zoom in and out, but no further than a 4x zoom.

Only one person could see the compression artifacts at a 1:1 zoom before 20:1 compression. At 20:1 all the experienced remote sensing people could see a few artifacts. The experienced GIS people could see the artifacts at a 2x zoom of 15:1, but only one of non-geospatial people (a business student) could see the artifacts at 2x 15:1. (Later she decided to work as a research assistant for me and is still in the geospatial industry).

My point is… many in the geospatial industry push the 2 – 2.5x file size saving when using NL compression; when many trained geospatial people cannot see a compression artifacts in 3-band image compressions below 10:1 unless they zoom in to 4x. (Sure, we all know of the exceptions; the remote sensing, and photogrammetry experts duly noted above; but these are the exception, not the rule.)

So why not compress at a VL level when you are not doing precise remote sensing or photogrammetry? The medical imaging industry is gravitating on between 8:1 and12:1. Is what we are doing in the geospatial world so important we have to preserve more precision than the medical imaging? Are we protecting more precision than our accuracy supports?

I for one think we are wasting space and time when we do not compress to at least the VL compression level.

A final note, I am running tests to know at what level we can wavelet compress data and not affect autocorrelation, classification, and vegetation indices. Have any of you dared such tests? Care to let us know?

Hexagon Aquires Intergraph

Hexagon, the parent company of Leica Geosystems (the parent company of ERDAS) announced today it acquired Intergraph yesterday, July 6, 2010. With this acquisition, Hexagon owns the two most respected areal imaging / LIDAR sensor companies. This capability along with the high precision GPS, CAD, GIS and Remote Sensing software capabilities, and different market access should make for interesting synergies among Hexagon's geospatial companies over the next few years.


Hexagon Announcement

Intergraph Announcement

With this acquisition, has Ola Rollen, CEO of Hexagon positioned Hexagon as the largest geospatial organization on the planet? Looking at the Intergraph, Leica Geosystems, NovAtel and ERDAS numbers, the case can surely be proposed.

Ola has told his ERDAS and Leica Geosystems people Hexagon is dedicated to the software business. This move puts real teeth behind his comments and right in the middle of the geospatial industry.

Ola Rollen's Press Conference

Hexagon will move Intergraph's sensor unit over to Leica Geosystems and ERDAS, Inc. over to Intergraph. This will consolidate geospatial hardware under Leica Geosystems and geospatial software under Intergraph.

Stay Tuned!