The Megapixel Race And Its Clear Winner

Like any Moore’s Law-inspired race, the megapixel race in digital cameras in the late 1990s and into the 2000s was a harsh battleground for every manufacturer. With the development of the smartphone, it became a war on two fronts, with Samsung eventually cramming twenty megapixels into a handheld. Although no clear winner of consumer-grade cameras was ever announced (and Samsung ended up reducing their flagship phone’s cameras to sixteen megapixels for reasons we’ll discuss) it seems as though this race is over, fizzling out into a void where even marketing and advertising groups don’t readily venture. What happened?

The Technology

A brief overview of Moore’s Law predicts that transistor density on a given computer chip should double about every two years. A digital camera’s sensor is remarkably similar, using the same silicon to form charge-coupled devices or CMOS sensors (the same CMOS technology used in some RAM and other digital logic technology) to detect photons that hit it. It’s not too far of a leap to realize how Moore’s Law would apply to the number of photo detectors on a digital camera’s image sensor. Like transistor density, however, there’s also a limit to how many photo detectors will fit in a given area before undesirable effects start to appear.

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CMOS Image Sensor Mechanism Illustration, By User:たまなるたみ – drawing created myself, GPL, https://commons.wikimedia.org/w/index.php?curid=371238. Note that each pixel has its own amplifier.

Image sensors have come a long way since video camera tubes. In the ’70s, the charge-coupled device (CCD) replaced the cathode ray tube as the dominant video capturing technology. A CCD works by arranging capacitors into an array and biasing them with a small voltage. When a photon hits one of the capacitors, it is converted into an electrical charge which can then be stored as digital information. While there are still specialty CCD sensors for some niche applications, most image sensors are now of the CMOS variety. CMOS uses photodiodes, rather than capacitors, along with a few other transistors for every pixel. CMOS sensors perform better than CCD sensors because each pixel has an amplifier which results in more accurate capturing of data. They are also faster, scale more readily, use fewer components in general, and use less power than a comparably sized CCD. Despite all of these advantages, however, there are still many limitations to modern sensors when more and more of them get packed onto a single piece of silicon.

While transistor density tends to be limited by quantum effects, image sensor density is limited by what is effectively a “noisy” picture. Noise can be introduced in an image as a result of thermal fluctuations within the material, so if the voltage threshold for a single pixel is so low that it falsely registers a photon when it shouldn’t, the image quality will be greatly reduced. This is more noticeable in CCD sensors (one effect is called “blooming“) but similar defects can happen in CMOS sensors as well. There are a few ways to solve these problems, though.

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A sunrise picture taken with an entry-level DSLR at 1600 ISO. At this sensitivity, noise in the clouds can be seen in the form of random fluctuations of some pixels. This effect would be mitigated by a camera with a larger sensor, a lower sensor sensitivity with a longer shutter speed (which would blur the turbine blades) or a scene with more light. Photo  © 2016 by Bryan Cockfield

 

First, the voltage threshold can be raised so that random thermal fluctuations don’t rise above the threshold to trigger the pixels. In a DSLR, this typically means changing the ISO setting of a camera, where a lower ISO setting means more light is required to trigger a pixel, but that random fluctuations are less likely to happen. From a camera designer’s point-of-view, however, a higher voltage generally implies greater power consumption and some speed considerations, so there are some tradeoffs to make in this area.

Another reason that thermal fluctuations cause noise in image sensors is that the pixels themselves are so close together that they influence their neighbors. The answer here seems obvious: simply increase the area of the sensor, make the pixels of the sensor bigger, or both. This is a good solution if you have unlimited area, but in something like a cell phone this isn’t practical. This gets to the core of the reason that most modern cell phones seem to be practically limited somewhere in the sixteen-to-twenty megapixel range. If the pixels are made too small to increase megapixel count, the noise will start to ruin the images. If the pixels are too big, the picture will have a low resolution.

There are some non-technological ways of increasing megapixel count for an image as well. For example, a panoramic image will have a megapixel count much higher than that of the camera that took the picture simply because each part of the panorama has the full mexapixel count. It’s also possible to reduce noise in a single frame of any picture by using lenses that collect more light (lenses with a lower f-number) which allows the photographer to use a lower ISO setting to reduce the camera’s sensitivity.

Gigapixels!

Of course, if you have unlimited area you can make image sensors of virtually any size. There are some extremely large, expensive cameras called gigapixel cameras that can take pictures of unimaginable detail. Their size and cost is a limiting factor for consumer devices, though, and as such are generally used for specialty purposes only. The largest image sensor ever built has a surface of almost five square meters and is the size of a car. The camera will be put to use in 2019 in the Large Synoptic Survey Telescope in South America where it will capture images of the night sky with its 8.4 meter primary mirror. If this was part of the megapixel race in consumer goods, it would certainly be the winner.

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LSST Image Sensor, By Todd Mason, Mason Productions Inc. / LSST Corporation – https://www.lsst.org/sites/default/files/photogallery/Camera_CU-full.jpg, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=52230238

With all of this being said, it becomes obvious that there are many more considerations in a digital camera than just the megapixel count. With so many facets of a camera such as physical sensor size, lenses, camera settings, post-processing capabilities, filters, etc., the megapixel number was essentially an easy way for marketers to advertise the claimed superiority of their products until the practical limits of image sensors was reached. Beyond a certain limit, more megapixels doesn’t automatically translate into a better picture. As already mentioned, however, the megapixel count can be important, but there are so many ways to make up for a lower megapixel count if you have to. For example, images with high dynamic range are becoming the norm even in cell phones, which also helps eliminate the need for a flash. Whatever you decide, though, if you want to start taking great pictures don’t worry about specs; just go out and take some photographs!

(Title image: VISTA gigapixel mosaic of the central parts of the Milky Way, produced by European Southern Observatory (ESO) and released under Creative Commons Attribution 4.0 International License. This is a scaled version of the original 108,500 x 81,500, 9-gigapixel image.)

Bringing The Best Laptop Ever Made Back To Life

Eight or nine years ago, Apple was on top of the world. The iPhone just revolutionized phones, Apple was still making computers, and these computers were actually repairable. Of the late 2008/early 2009 MacBook Pro, iFixit said, “What an incredible machine. We are very impressed by the ease with which the new MacBook Pro came apart. This machine should be a joy to work on”. Apple has come a long way since then.

macbook-reflow-shield[DocDawning] has a bit of a Mac hoarding problem, and frequently pays $20 for broken laptops of this vintage. Most of the time, the fix is simple: the RAM needs to be reseated, or something like that. Rarely, he comes across a machine that isn’t fixed so easily. The solution, in this case, is a deep dive into heat guns and thermal management. How do you bring a laptop back from the dead? [Dawning] shows you how.

Like the old XBox towel hack, the first thing to look for in dead electronics is broken solder balls. Of course, actually looking at broken solder balls is pretty hard, so you might as well just get out a heat gun and go at it. That’s exactly what [Dawning] did. With the clever application of an aluminum takeout tray to direct the heat flow, he blasted each of these chips with enough heat to hopefully melt all the balls.

With that, a working MacBook Pro was just a liberal application of thermal paste away. From $20 at the scrap heap to a working computer, [Dawning] did it. He successfully resuscitated a broken computer.

Paper and lighter IR remote control

Faking TV Remote Control With Paper And A Lighter

Cut slots into a piece of paper to represent the IR remote control bitstream for putting your TV into standby. Insert it between your TV’s IR receiver and the flame from a lighter, and pull the slots along to generate the coded pattern. Get it just right and you have a paper and lighter remote control. That’s just what [ViralVideoLab] did and you can see it in action in the video below.

Think of this as just the germ of an idea. Imagine how you’d automate this and extend it to include more commands. A wheel with the various bitstreams cut into the circumference comes to mind. A servo would turn the wheel to the desired command and something else would fire up the lighter just as the slots pass by. Now take it a little further. You already have a remote control with keypad and IR light. Hack that to talk to a microcontroller which would control the servo and the IR light. And there you go. A useless but fun hack (hint hint).

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