Definition of pixels for the world's biggest photos

I shoot lots of large panoramas, and the arrival of various cheaper robotic mounts to shoot them, such as the Gigapan Epic Pro and the Merlin/Skywatcher (which I have) has resulted in a bit of a “mine’s bigger than yours” contest to take the biggest photo. Some would argue that the stitched version of the Sloane Digital Sky survey, which has been rated at a trillion pixels, is the winner, but most of the competition has been on the ground.

Many of these photos have got special web sites to display them such as Paris 26 gigapixels, the rest are usually found at the Gigapan.org site where you can even view the gigapans sorted by size to see which ones claim to be the largest.

Most of these big ones are stitched with AutopanoPro, which is the software I use, or the Gigapan stitcher. The largest I have done so far is smaller, my 1.4 gigapixel shot of Burning Man 2010 which you will find on my page of my biggest panoramas which more commonly are in the 100mp to 500mp range.

The Paris one is pretty good, but some of the other contenders provide a misleading number, because as you zoom in, you find the panorama at its base is quite blurry. Some of these panoramas have even just been expanded with software interpolation, which is a complete cheat, and some have been shot at mixed focal length, where sections of the panorama are sharp but others are not. I myself have done this, for example in my Gigapixel San Francisco from the end of the Golden Gate I shot the city close up, but shot the sky and some of the water at 1/4 the resolution because there isn’t really any fine detail in the sky. I think this is partially acceptable, though having real landscape features not at full resolution should otherwise disqualify a panorama. However, the truth is that sections of sky perhaps should not count at all, and anybody can make their panorama larger by just including more sky all the way to the zenith if they choose to.

There is a difficult craft to making such large photos, and there are also aesthetic elements. To really count the pixels for the world’s largest photos, I think we should count “quality” pixels. As such, sky pixels are not generally quality pixels, and distant terrain lost in haze also does not provide quality pixels. The haze is not the technical fault of the photographer, but it is the artistic fault, at least if the goal is to provide a sharp photo to explore. You get rid of haze only through the hard work of being there at the right time, and in some cities you may never get a chance.

Some of the shots are done through less than ideal lenses, and many of them are done use tele-extenders. These extenders do get more detail but the truth is a 2x tele-extender does not provide 4 times as many quality pixels. A common lens today is a 400mm with a 2x extender to get 800mm. Fairly expensive, but a lot cheaper than a quality 800mm lens. I think using that big expensive glass should count for more in the race to the biggest, even though some might view it as unfair. (A lens that big and heavy costs a ton and also weighs a lot, making it harder to get a mount to hold it and to keep it stable.) One can get very long mirror “lens” setups that are inexpensive, but they don’t deliver the quality, and I don’t believe work done with them should score as high as work with higher quality lenses. (It may be the case that images from a long telescope, which tend to be poor, could be scaled down to match the quality of a shorter but more expensive lens, and this is how it should be done.)

Ideally we should seek an objective measure of this. I would propose:

  • There should be a sufficient number of high contrast edges in the image — sharp edges where the intensity goes from bright to dark in the space of just 1 or 2 pixels. If there are none of these, the image must be shrunk until there are.
  • The image can then be divided up into sections and the contrast range in each evaluated. If the segment is very low contrast, such as sky, it is not counted in the pixel count. Possibly each block will be given a score based on how sharp it is, so that background items which are hazy count for more than nothing, but not as much as good sharp sections.
  • I believe that to win a pano should not contain gross flaws. Examples of such flaws include stripes of brightness or shadow due to cloud movement, big stitching errors and checkerboard patterns due to bad overlap or stitching software. In general that means manual exposure rather than shots where the stitcher tries to fix mixed exposures unless it does it undetectably.

Some will argue with the last one in particular, since for some the goal is just to get as many useful pixels as possible for browsing around. Gigapixel panoramas after all are only good for zooming around in with a digital viewer. No monitor can display them and sometimes even printing them 12 feet high won’t show all their detail, and people rarely do that. (Though you can see my above San Francisco picture as the back wall of a bar in SF.) Still, I believe it should be a minimum bar than when you look at the picture at more normal sizes, or print it out a few feet in size, it still looks like an interesting, if extremely sharp, picture.

Ideally an objective formula can be produced for how much you have to shrink what is present to get a baseline. It’s very rare that any such panorama not contain a fair number of segments with high contrast edges and lines in them. For starters, one could just put in the requirement that the picture be shrunk until you have a frame that just about anybody would agree is sharp like an ordinary quality photo when viewed 1:1. Ideally lots of frames like that, all over the photo.

Under these criteria a number of the large shots on gigapan fall short. (Though not as short as you think. The gigapan.org zoom viewer lets you zoom in well past 1:1, so even sharp images are blurry when zoomed in fully. On my own site I set maximum zoom at 200%.)

These requirements are quite strict. Some of my own photos would have to be shrunk to meet these tests, but I believe the test should be hard.

Your criteria sound

Your criteria sound remarkably like what a compression algorithm does. Try using JPEG2000 or maybe even straight JPEG on the image with a quality threshold and see whta you get. If you JPEG the tiles, you can set a minimum tile size threshold to be counted.

This could make some sense

And some of the wavelet compressors might do even better. The real goal is sort of subjective — “At 1:1, does it still look like a decent photo?” and the desire is to get an objective test of that. Measuring entropy as compression algorithms do might be indeed a good test.

Comparing megapixels

The issue of megapixels vs quality has been an issue for digital photography from the get-go. Marketers have always wanted as many megapixels as possible, even if they were only accurately representing the errors of the lens (e.g. hi-res mobile phones), or worse, actually increasing noise. Any focus simply on the numbers is superficial and in the long run it will be actual quality that counts. Like with cameras, once the novelty and the hype wears off, surely, in spite of some early attention grabbing, it will eventually come back to the actual quality and level of visible detail available to the user.

Quality of pixels

Indeed, and some camera vendors have started on a path of selling quality pixels rather than quantity. However, there are two reasons they do this. One is that in small cameras, having 15 megapixels makes the pixels so small that they are going smaller than the lens can deliver, and are thus wasted. The other reason is that there is more noise on smaller pixels. The best images come from the big heavy DSLRs like I carry which have both big pixels and a lot of them, and fancy lenses that are up to the task.

However, this is nothing like what we are seeing in some of the gigapixel competition, where sometimes it’s getting ridiculous and claims are being made of an order of magnitude more pixels than are really there. Even the point and shoot cameras that claimed to have 12 megapixels still really had 6, for example.

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