Old TV To RGB

As CRT televisions have faded from use, it’s become important for retro gaming enthusiasts to get their hands on one for that authentic experience. Alongside that phenomenon has been a resurgence of some of the hacks we used to do to CRT TV sets back in the day, as [Adrian’s Digital Basement] shows us when he adds an RGB interface to a mid-1990s Sony Trinitron.

Those of us lucky enough to have lived in Europe at the time were used to TVs with SCART sockets by the mid-1990s so no longer needed to plumb in RGB signals, but it appears that Americans were still firmly in the composite age. The TV might have only had a composite input, but this hack depends on many the video processor chips of the era having RGB input pins. If your set has a mains-isolated power supply then these pins can be hooked up with relative ease.

In the case of this little Sony, the RGB lines were used by the integrated on-screen display. He takes us through the process of pulling out these lines and interfacing to them, and comes up with a 9-pin D connector with the same pinout as a Commodore monitor, wired to the chip through a simple RC network and a sync level divider. There’s also a switch that selects RGB or TV mode, driving the OSD blanking pin on the video processor.

We like this hack just as much as we did when we were applying it to late-80s British TV sets, and it’s a great way to make an old TV a lot more useful. You can see it in the video below the break, so get out there and find a late-model CRT TV to try it on while stocks last!

Unsurprisingly, this mod has turned up here a few times in the past.

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Norm Abram Is Back, And Thanks To AI, Now In HD

We’ve said many times that while woodworking is a bit outside our wheelhouse, we have immense respect for those with the skill and patience to turn dead trees into practical objects. Among such artisans, few are better known than the legendary Norm Abram — host of The New Yankee Workshop from 1989 to 2009 on PBS.

So we were pleased when the official YouTube channel for The New Yankee Workshop started uploading full episodes of the classic DIY show a few months back for a whole new generation to enjoy. The online availability of this valuable resource is noteworthy enough, but we were particularly impressed to see the channel start experimenting with AI enhanced versions of the program recently.

Note AI Norm’s somewhat cartoon-like appearance.

Originally broadcast in January of 1992, the “Child’s Wagon” episode of Yankee Workshop was previously only available in standard definition. Further, as it was a relatively low-budget PBS production, it would have been taped rather than filmed — meaning there’s no negative to go back and digitize at a higher resolution. But thanks to modern image enhancement techniques, the original video could be sharpened and scaled up to 1080p with fairly impressive results.

That said, the technology isn’t perfect, and the new HD release isn’t without a few “uncanny valley” moments. It’s particularly noticeable with human faces, but as the camera almost exclusively focuses on the work, this doesn’t come up often. There’s also a tendency for surfaces to look smoother and more uniform than they should, and reflective objects can exhibit some unusual visual artifacts.

Even with these quirks, this version makes for a far more comfortable viewing experience on today’s devices. It’s worth noting that so far only a couple episodes have been enhanced, each with an “AI HD” icon on the thumbnail image to denote them as such. Given the computational demands of this kind of enhancement, we expect it will be used only on a case-by-case basis for now. Still, it’s exciting to see this technology enter the mainstream, especially when its used on such culturally valuable content. Continue reading “Norm Abram Is Back, And Thanks To AI, Now In HD”

How To Roll Your Own Custom Object Detection Neural Network

Real-time object detection, which uses neural networks and deep learning to rapidly identify and tag objects of interest in a video feed, is a handy feature with great hacker potential. Happily, it’s also possible to make customized CNNs (convolutional neural networks) tailored for one’s own needs, and that process just got easier thanks to some new documentation for the Vizy “AI camera” by Charmed Labs.

Raspberry Pi-based Vizy camera

Charmed Labs has been making hacker-friendly machine vision devices for a long time, and the Vizy camera impressed us mightily when we checked it out last year. Out of the box, Vizy has a perfectly functional object detector application that runs locally on the device, and can detect and tag many common everyday objects in real time. But what if that default application doesn’t quite meet one’s project needs? Good news, because it’s possible to create a custom-trained CNN, and that process got a lot more accessible thanks to step-by-step examples of training a model to recognize hands doing rock-paper-scissors.

Person and cat with machine-generated tags identifying them
Default object detection works well, but sometimes one needs custom results.

The basic process is this: Start with a variety of images that show the item of interest. Then identify and label the item of interest in each photo. These photos (a “training set”) are then sent to Google Colab, which will be used to generate a neural network. The resulting CNN model can then be downloaded and used, to see how well it performs.

Of course things rarely work perfectly the first time around, so at this point it’s pretty common for some refinement to be needed to increase accuracy. Luckily there are a number of tools to help do this without creating a new model from scratch, so it’s just a matter of tweaking until things perform acceptably.

Google Colab is free and the resulting CNNs are implemented in the TensorFlow Lite framework, meaning it’s possible to use them elsewhere. So if custom object detection has been holding up a project idea of yours, this might be what gets you over that hump.

RCA’s Clear Plastic TV Wowed Crowds In 1939

In the United States in 1939, television sets still had a long way to go before they pretty much sold themselves. Efforts to do just that are what led to RCA’s Lucite Phantom Telereceiver, which aimed to show people a new way to receive broadcast media.

Created for the 1939 World’s Fair, the TRK-12 Lucite Phantom Telereceiver introduced people to the concept of television. Production models were housed in contemporary wood cabinets, but the clear acrylic (itself also a relatively new thing) units allowed curious potential customers to gaze within, and see what was inside these devices.

One interesting feature is the vertically-mounted cathode ray tube, which reflects off a mirror in the top cover of the cabinet for viewing. This meant that much of the bulk of the TRK-12 could be vertical instead of horizontal. Important, because the TRK-12 was just over a meter tall and weighed 91 kilograms (or just over 200 lbs.)

Clearly a luxury item, the TRK-12 sold for $600 which was an eye-watering sum for the time. But it was a glimpse of the future, and as usual, the future is made available a few ticks early to those who can afford the cost.

Want to see one in person? You might be in luck, because an original resides at the MZTV Museum of Television in Toronto, Canada.

Arduino Does SDI Video With FPGA Help

If you are running video around your home theater, you probably use HDMI. If you are running it in a professional studio, however, you are probably using SDI, Serial Digital Interface. [Chris Brown] looks at SDI and shows a cheap SDI signal generator for an Arduino.

On the face of it, SDI isn’t that hard. In fact, [Chris] calls it “dead simple.” The problem is the bit rate which can be as high as 1.485 Gbps for the HD-SDI standard. Even for a super fast processor, this is a bit much, so [Chris] turned to the Arduino MKR Vidor 4000. Why? Because it has an FPGA onboard. Alas, the FPGA can’t do more than about 200 MHz, but that’s fast enough to drive an external Semtech GS296t2 serializer which is made to drive SDI signals.

The resulting project contains the Arduino, the serializer, a custom PCB, and both FPGA and microcontroller code. While the total cost of the project was a little under $200, that’s still better than the $350 to $2000 for a commercial SDI signal generator.

If you want to play along, the files are out on GitHub. We used the Vidor back in 2018 when it first came out. If you need a quick start on FPGAs, there’s always our boot camp.

Generating PAL Video With A Heavily Overclocked Pi Pico

Barely a week goes by without another hack blessing the RP2040 with a further interfacing superpower. This time it’s the turn of the humble PAL standard composite video interface. As many of us of at least a certain vintage will be familiar with, the Phase Alternate Line (PAL to friends) standard was used mainly in Europe (not France, they used SECAM like Russia, China, and co) and Australasia, and is a little different from the much earlier NTSC standard those in the US may fondly recollect. Anyway, [Fred] stresses that this hack isn’t for the faint-hearted, as the RP2040 needs one heck of an overclock (up to 312 MHz, some 241% over stock) to be able to pull off the needed amount of processing grunt. This is much more than yet another PIO hack.

The dual cores of the RP2040 are really being pushed here. The software is split into high and low-level functions, with the first core running rendering the various still images and video demos into a framebuffer. The second core runs in parallel and deals with all the nitty-gritty of formatting the frame buffer into a PAL-encoded signal, which is then sucked out by the DMA and pushed to the outside world via the PIO. There may be a few opportunities for speeding the code up even more, but [Fred] has clearly already done a huge amount of work there, just to get it working at all. The PIO code itself is very simple but is instructive as a good example of how to use multiple chained DMA channels to push data through the PIO at the fastest possible rate.

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Closeup of a film restorer's hand holding a 35mm film print to check for defects as it goes into a film scanner

35mm Film Restoration Process Explained

For a large part of the 20th century, motion pictures were distributed on nitrate film. Although cheaper for the studios, this film was highly flammable and prone to decay. On top of that, most film prints were simply discarded once they had been through their run at the cinema, so a lot of film history has been lost.

Sometimes, the rolls of projected film would be kept by the projectionist and eventually found by a collector. If the film was too badly damaged to project again, it might still get tossed. Pushing against this tide of decay and destruction are small groups of experts who scan and restore these films for the digital age.

still showing the difference in quality between a 16mm print of a 35mm animated movie and a new scan of the 35mm original
The quality difference between a smaller-format print and the original restored negative can be startling

The process is quite involved – starting with checking every single frame of film by hand and repairing any damaged perforations or splices that could come apart in the scanner. Each frame is then automatically scanned at up to 10K resolution to future-proof the process before being painstakingly digitally cleaned.

The real expertise is in knowing what is damage or dirt, and what is the character of the original film. Especially in stop-motion movies, the subtle changes between frames are really part of the original, so the automatic clean-up tools need to be selectively reined in so as not to lose the charm and art of the film-makers.

The results are quite astonishing and we all have teams like this to thank for protecting our cultural heritage.

If you’re interested in watching the process, then check out the video after the break. If you fancy a go at automatic film digitising yourself (preferably not on unique historical prints!) then we’ve shown projects to do just that in the past.

Thanks to [Cliff Claven] for the tip.

Continue reading “35mm Film Restoration Process Explained”