Many of the image formats we’re familiar with have been around for decades. We all know JPEG and PNG like the back of our hands, and GIF even had a bit of a resurgence when tiny annoying looping animations became cool again on social media. There has been a growth in newer formats, though, like WEBP, and AVIF, with various platforms and software companies adopting them for greater efficiency, performance, and image quality.
Amongst the new crop of formats vying for relevance is JPEG XL. It was touted as a format that improved on what JPEG could do in the lossy compression space, while also adding on a lossless mode. There were grand hopes that it would become a universal replacement for all existing raster formats on the Web. A major problem was that Google was seemingly against it, only to backflip—with the format now slated to hit the big time with renewed backing from the search giant. So what’s going on?
The lossy compression algorithm used by JPEG was useful for those on the early Internet not only because it enabled pictures to be shared easier, but because it allows a low-resolution version of the image to load first. This meant that users could make out the gist of an image before it finished downloading. This was a great feature for those on slow connections, but it hides some other capabilities of this image format as well.
Rather than effectively splitting up the image into chunks, each with successive amounts of detail, [maurycyz]’s project shows that this can be exploited to load more than one picture. The first is loaded into this lower-resolution area, with a second unrelated picture showing up once the higher-resolution information is available. Essentially this makes a one-way .gif of sorts. Though this method is only capable of loading about nine frames, which is not enough for much animation. Further limiting things is that there’s no way to encode timing data, so on fast computers with fast connections the animation could load faster than a user could see.
Still, it’s an interesting quirk of this older image standard, one which still is in widespread use today. And it’s also true that it’s hard to say in what ways various technologies will be used in the future. JPEG images have also been the subject of some artistic projects that might not have been possible without the JPEG standard itself, and even as other formats have tried to supplant it, it still maintains its firm grip on the images on the Internet. More JPEG, please!
As the video explains, you generally can’t simply throw a JPEG into Notepad and start making changes all willy nilly. That’s because it’s very easy to wreck key pieces of the image format that are required to render it as an image. Particularly because Notepad likes to sanitize things like line endings which completely mess up the structure of the file. Instead, you’re best off using a binary editor that will only change specific bytes in the image when you tell it to. Do this, and you can glitch out an image in all kinds of fun digital ways… or ruin it completely. Your choice!
If you’d like to tinker around with this practice, [Patrick] has made a tool for just that purpose. Jump over to the website, load the image of your choice, and play with it to your heart’s content.
This practice is often referred to as “datamoshing,” which is a very cool word, or “databending,” which isn’t nearly as good. We’ve explored other file-format hacks before, too, like a single file that can be opened six different ways. Video after the break.
Docker and other containerization applications have changed a lot about the way that developers create new software as well as how they maintain virtual machines. Not only does containerization reduce the system resources needed for something that might otherwise be done in a virtual machine, but it standardizes the development environment for software and dramatically reduces the complexity of deploying on different computers. There are some other tricks up the sleeves as well, and this project called PI-CI uses Docker to containerize an entire Raspberry Pi.
The Pi container emulates an entire Raspberry Pi from the ground up, allowing anyone that wants to deploy software on one to test it out without needing to do so on actual hardware. All of the configuration can be done from inside the container. When all the setup is completed and the desired software installed in the container, the container can be converted to an .img file that can be put on a microSD card and installed on real hardware, with support for the Pi models 3, 4, and 5. There’s also support for using Ansible, a Docker automation system that makes administering a cluster or array of computers easier.
Docker can be an incredibly powerful tool for developing and deploying software, and tools like this can make the process as straightforward as possible. It does have a bit of a learning curve, though, since sharing operating system tools instead of virtualizing hardware can take a bit of time to wrap one’s mind around. If you’re new to the game take a look at this guide to setting up your first Docker container.
Much to the chagrin of Eastman Kodak, the world has moved on from chemical photography into the realm of digital, thanks to the ease of use and high quality of modern digital cameras. There are a few photographers here and there still using darkrooms and various chemical processes to develop film, and the most common of these use some type of chemistry based on silver to transfer images to paper. There are plenty of alternatives to silver, though, each with their unique style and benefits, like this rarely-used process that develops film using platinum.
This process, notable for its wide tonal range, delicate highlights, and rich blacks, produces only black and white photographs. But unlike its silver analog, it actually embeds the image into the paper itself rather than holding the image above the paper. This means that photographs developed in this manner are much more resilient and can last for much longer. There are some downsides to this method though, namely that it requires a large format camera and the negatives can’t be modified to produce various sized images in the same ways that other methods allow for. Still, the results of the method are striking for anyone who has seen one of these images in person.
As to why this method isn’t more common, [Matt Locke] describes a somewhat complicated history involving the use of platinum to create commercial fertilizers, which is an identical process to that of the creation of explosives, which were needed in great numbers at the same time this photographic method was gaining in popularity. While the amount of research and development that goes into creating weapons arguably generates some ancillary benefit for society, the effects of war can also serve to divert resources away from things like this.
For those of us who are a little older, the 90s seem like they were just a few years ago. The younger folks might think that the 90s were ancient history though, and they might be right as we’ve been hearing more bands like Pearl Jam and The Offspring playing on the classic rock stations lately. Another example of how long ago the 90s were is taking a look at the technological progress that has happened since then through the lens of things like this webcam from 1999, presuming you load up this custom user space driver from [benjojo].
Thankfully the driver for this infamous webcam didn’t need to be built completely from scratch. There’s a legacy driver available for Windows XP which showed that the camera still physically worked, and there’s also a driver for Linux which was used as a foundation to start working from. From there a USB interface was set up which allowed communication to the device. Not a simple task, but apparently much easier than the next steps which involve actually interpreting the information coming from the webcam. This is where a background in digital signal processing is handy to have. First, the resolution and packet size were sorted out which led to a somewhat recognizable image. From there a single monochrome image was pieced together, and then after deconstructing a Bayer filter and adding color, the webcam is back to its former 90s glory.
[benjojo] has hosted all of the code for this project on a GitHub page for anyone who still has one of these webcams sitting around in the junk drawer. The resolution and color fidelity are about what we’d expect for a 25-year-old device that predates Skype, Facebook, Wikipedia, and Firefox. And, while there are still some things that need to be tweaked such as the colors, white balance, and exposure, once that is sorted out the 90s and early 00s nostalgia is free to flood in.
This is the first we’ve heard of Parametric Press — a digital magazine with some deep dives into a variety of subjects (such as particle physics, “big data” and such) that have interactive elements or simulations of various types embedded within each story.
The first one that sprung up in our news feed is a piece by [Omar Shehata] on the humble JPEG image format. In it, he explains the how and why of the JPEG encoding process, allowing the reader to play with the various concepts along the way, in real time, within the browser.
RGB colour-space subsampling doesn’t affect each component to the same degree due to the human eye cone cell response. Also, the chroma components are much less affected than the luminance.
For those not familiar with the format, the first step (which is actually optional) to JPEG encoding is to transform the image from the RGB color space, into a YCbCr (luminance, chrominance) color space. Since the human eye is far more sensitive to luminance (brightness) differences than it is to Cb (chroma relative blueness) and Cr (chroma relative redness) differences, these latter two components can be subsampled by only storing a single value for each, in every 2×2 pixel matrix. JPEG allows other matrix sizes, but 2×2 is the most common.
This sets the scene for the clever bit, that comes next and allows more of that harder-to-perceive chroma information to be discarded. It’s fun to play with the chroma sub-sampling slider and see how the different colours are not equally affected, due to the relative sensitivities of the human eye cone cells.
Next, the three YCbCr components are treated independently to a discrete cosine transform and quantization. This transforms each 8×8 pixel block into 64 discrete spatial frequencies. The JPEG compression level (which you can change) affects how many of the upper-frequency components get discarded, and thus how much of the fine spatial detail gets discarded. This is the main source of JPEG image quality loss. Finally, the compressed blocks are delta encoded, where each subsequent block is coded as the difference from the previous one. Like chroma subsampling, this doesn’t offer any compression on its own but allows the subsequent run-length encoding to be more effective, giving more (lossless) compression. Finally, the whole lot is then Huffman compressed with a unique table stored in the JPEG header. So want to play with JPEGs some more? here’s the GitHub source.
If all of this theoretical stuff is a bit useless to you, perhaps you just want to decode some JPEGs, then here is a speedy library for just that.
