Today, our software group provided a set of major updates to our downlink operations team. It was the first major update in many months. One of the most anticipated features is smarter “stretch” algorithm for our color products (RDR Extras). As discussed in a previous post, a stretch (in image processing terms), is a mapping between one range of pixel values and another. In our case, it provides our viewers with a better-looking image up-front, with less need to adjust parameters in display software such as IAS (though this is still often very helpful when zoomed in). As always, the full range of original data is preserved in the RDR JP2.

Our former algorithm for the NOMAP and Quicklook products said that the pixel values above the brightest 0.1% and below the darkest 0.1% would be mapped to the extreme values, with a linear fit in between. For a majority of images, this was a good choice that showed excellent contrast but prevented too much saturation.

However, 0.1% (a thousandth) of a two Gigapixel image is still two million pixels. So if there were a particularly bright spot, like a rocky outcrop amid a field of dunes, or a particularly dark spot, like a cavern opening in a plain of boulders, then all the saturation would occur in that one area, washing it out completely, and lowering contrast everywhere else in the image. So the algorithm needed to be more adaptable. After a good deal of experimentation, the algorithm we settled on looks at the brightest and darkest pixels in a thumbnail version of the image, and uses those values for the extremes, instead of the values at 0.1%. We shrink a copy of each color band to 1/11th the original scale. Pixel values in the original below the darkest in the thumbnail are mapped to pure black, while pixel values above the highest are mapped to pure white. The stretched bands are then merged to make the color image. Hence, a bright or dark spot smaller than 1/11th x 1/11th of the image size will no longer dominate the stretch.

What this ultimately means is, our RDR Extras now show more detail in areas that would be completely washed out by the old algorithm.

For example, in this ‘cave’ image, the left is from the original RGB.NOMAP.JP2, while the right is the same product using the new algorithm. As you can see, previously you could not tell if there was a floor to the hole or if it sloped away to greater depths.

The new algorithm is used strictly for the JP2’s; the browse and thumb are already scaled down enough that it would not make a substantial difference with them. The new algorithm went into effect today; coincidentally we just started orbit 8000. Images with the new stretch will likely appear in upcoming weekly releases and we plan to reprocess everything with this change (and improved calibration) during the summer.

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Here are 66 false-color images from the 1400 orbit range.

View Images

PSP_001406_2680 looks like the higher relief was saturated (too bright for the camera settings), possibly due to CO2 frost cover.

PSP_001432_2015 is really cool; it’s on the edge of Olympus Mons, on the steep scarp leading to the much more gradual rise of the shield volcano. The rippled rolling dunes in PSP_001432_2610 are in striking contrast to the rocky floors between them. Check out the amazing slot canyons fractures along the left side in PSP_001440_2175.

The atmospheric haze in PSP_001444_2610 is incredible, though it does screw up the color registration on the bottom half of the image. This is 30 degrees East of the aforementioned dune location, but the same type of terrain. On some of these images, there will be CTX (Context camera) images. With similar haze conditions, over on UnmannedSpaceflight.com, Nirgal shows a colorized CTX image from MRO orbit 3624 for which there is a HiRISE view.

There are so many other great images in this set. The Holden Crater image deserves special mention. This area is on the candidate list for MSL, as mentioned in a previous post. A stereo print was made of this region at about the same resolution you see here; it was amazingly sharp, like looking into a scale model or diorama.

Again, feel free to post your favorites here in the comments.

Updated (2008-Apr-10)

With the color images, dynamic range becomes more important then ever before. The DRA (Dynamic Range Adjustment) options of the IAS viewer are a great boon when looking at these images.

DRA performs what image processing folks call a “stretch.” A stretch takes some range of pixel values from the file and maps it onto a new range for the screen. To take an example, consider an image that appears over-exposed: much of the information is in the upper range of pixel values and you will have trouble distinquishing any detail. If the over-exposed pixels are not completely saturated (i.e. they don’t all have the maximum value) then a stretch that reduces brightness can reveal this otherwise hidden detail.

HiRISE has a very high signal-to-noise ratio, and our targeting specialists do a very good job choosing camera settings (which they do individually for each and every image) so completely saturated pixels are very rare.

But this also means that a stretch that works well over the entire image (a global stretch) may not be the best, the optimal stretch, for any one sub-image area that you are viewing. This is where the Auto DRA function in IAS becomes critical.

The button (shown below) is located on the right-hand side of the toolbar. A single click will do a stretch based only on the pixels you are viewing. This can bring out detail in shadow–amazingly, there is enough ambient light scattering around in the thin atmosphere to illuminate those scenes (and HiRISE is sensitive enough to pick enough of it up). It can also bring out detail in bright areas of over-exposure. For the color images in particular this can make things look a whole lot better.

Another factor plays a part in this. By default, the IAS viewer performs a global DRA when the image is loaded. As seen in the screenshot below, there are areas in our image that can skew the stretch. The large red rectangle is an area where the red CCDs start imaging before the blue-green. The IRB images often will have a cyan region where one of the IR CCDs was too noisy. We have elected to keep these areas in our images.

When in a sub-area, hit the Auto DRA button and the image should be drastically improved, as you can see in this final screenshot.

DRA early and often!

Saturday’s front-page of the Arizona Daily Star featured a good article about HiRISE with a humorous photo of Chris S. shoulder surfing as Anjani P. worked.

This is of course a typical scene as these images are coming down. Someone will be “driving”, so to speak: browsing, “surfing”, zooming, panning, contrast stretching and more, using the amazing image processing tools that our partners at the USGS in Flagstaff have developed. And someone else (or two, or three, or a dozen) will be standing over their shoulders, watching and collaborating. Or in Chris’s case, probably making jokingly snide comments.

But it makes for a world of difference between what you, out there, the public, experience and what we experience. Bridging that gap is a challenge for our public outreach team as the mission continues. We’ve got the benefit of having the brightest minds in planetary geology offering live commentary and analysis using the finest tools and top-of-the-line workstations. The experience of seeing the image is very dynamic; jumping between resolutions, zooming in to dunes and boulders and rocky outcrops, “stretching” the image to pull out detail hidden in dark shadow or blended in on bright surfaces.

I guess what I’m trying to say is that while our excitement is evident from the posts here, communicating exactly why we’re excited is much more difficult. Unless you could be here shoulder surfing….