I… I’m speechless.
Although, as a professional internet nitpicker, I have top s say I’m surprised he didn’t go into color theory more. Easy 45 minutes of additional content there. (I guess he touched on it, but there’s so much more.)
Now I remember why I looked at lasagna cat a few years ago and, after a minute or two, decided to set it aside. There’s a whole lifetime’s worth of content to drown in.
I almost thought I had written your comment and completely forgot about it. No, I just almost made the exact comment and want that hour of my life back.
If there was some over the top racist rant, I sure didn’t see it. And the admin pushing for the defederation sounds so bizarre. Bizarre is the best word I could come up with because “petty” makes me think it was like high school politics. This is closer to a grade school sandbox argument.
The worst I saw was “defedfags” and it was used in a way that was meant to highlight how they never said anything offensive. Like saying, “If you thought what I said before was offensive, let’s see how you respond to something intended to be negative.”
The crazy thing is that the decision is being made because the admin just liked a post. It’s not even because of the post content - which has nothing controversial and appeared maybe 8 times in my Lemmy/kbin feed yesterday.
Editing to add that this is the article: https://kbin.social/search?q=wakeup+call
At first glance, I probably thought JXL was another attempt at JPEG2000 by a few bitter devs, so I had ignored it.
Yeah, my examples/description was more intended to be conceptual for folks that may not have dealt with the nitty gritty. Just mental exercises. I’ve only done a small bit of image analysis, so I have a general understanding of what’s possible, but I’m sure there are folks here (like you) that can waaay outclass me on details.
These intermediate-to-deep dives are very interesting. Not usually my cup of tea, but this does seem big. Thanks for the info.
(fair warning - I go a little overboard on the examples. Sorry for the length.)
No idea on the details, but apparently it’s more efficient for multithreaded reading/writing.
I guess that you could have a few threads reading the file data at once into memory. While one CPU core reads the first 50% of the file, and second can be reading in the second 50% (though I’m sure it’s not actually like that, but as a general example). Image compression usually works some form of averaging over an area, so figuring out ways to chop the area up, such that those patches can load cleanly without data from the adjoining patches is probably tricky.
I found this semi-visual explanation with a quick google. The image in 3.4 is kinda what I’m talking about. In the end you need equally sized pixels, but during compression, you’re kinda stretching out the values and/or mapping of values to pixels.
Not an actual example, but highlights some of the problems when trying to do simultaneous operations…
Instead of pixels 1, 2, 3, 4 being colors 1.1, 1.2, 1.3, 1.4, you apply a function that assigns the colors 1.1, 1.25, 1.25, 1.4. You now only need to store the values 1.1, 1.25, 1.4 (along with location). A 25% reduction in color data. If you wanted to cut that sequence in half for 2 CPUs with separate memory blocks to read at once, you lose some of that optimization. Now CPU1 and CPU2 need color 1.25, so it’s duplicated. Not a big deal in this example, but these bundles of values can span many pixels and intersect with other bundles (like color channels - blue can be most efficiently read in 3 pixels wide chunks, green 2 pixel wide chunks, and red 10 pixel wide chunks). Now where do you chop those pixels up for the two CPUs? Well, we can use our “average 2 middle values in 4 pixel blocks” approach, but we’re leaving a lot of performance on the table with empty or useless values. So, we can treat each of those basic color values as independent layers.
But, now that we don’t care how they line up, how do we display a partially downloaded image? The easiest way is to not show anything until the full image is loaded. Nothing nothing nothing Tada!
Or we can say we’ll wait at the end of every horizontal line for the values to fill in, display that line, then start processing the next. This is the old waiting for the picture to slowly load in 1 line at a time cliche. Makes sense from a human interpretation perspective.
But, what if we take 2D chunks and progressively fill in sub-chunks? If every pixel is a different color, it doesn’t help, but what about a landscape photo?
First values in the file: Top half is blue, bottom green. 2 operations and you can display that. The next values divide the halves in half each. If it’s a perfect blue sky (ignoring the horizon line), you’re done and the user can see the result immediately. The bottom half will have its values refined as more data is read, and after a few cycles the user will be able to see that there’s a (currently pixelated) stream right up the middle and some brownish plant on the right, etc. That’s the image loading in blurry and appearing to focus in cliche.
All that is to say, if we can do that 2D chunk method for an 8k image, maybe we don’t need to wait until the 8k resolution is loaded if we need smaller images for a set. Maybe we can stop reading the file once we have a 1024x1024 pixel grid. We can have 1 high res image of a stoplight, but treat is as any resolution less than the native high res, thanks to the progressive loading.
So, like I said, this is a general example of the types of conditions and compromises. In reality, almost no one deals with the files on this level. A few smart folks write libraries to handle the basic functions and everyone else just calls those libraries in their paint, or whatever, program.
Oh, that was long. Um, sorry? haha. Hope that made sense!
Oh, I’ve just been toying around with Stable Diffusion and some general ML tidbits. I was just thinking from a practical point of view. From what I read, it sounds like the files are smaller at the same quality, require the same or less processor load (maybe), are tuned for parallel I/O, can be encoded and decoded faster (and there being less difference in performance between the two), and supports progressive loading. I’m kinda waiting for the catch, but haven’t seen any major downsides, besides less optimal performance for very low resolution images.
I don’t know how they ingest the image data, but I would assume they’d be constantly building sets, rather than keeping lots of subsets, if just for the space savings of de-duplication.
(I kinda ramble below, but you’ll get the idea.)
Mixing and matching the speed/efficiency and storage improvement could mean a whole bunch of improvements. I/O is always an annoyance in any large set analysis. With JPEG XL, there’s less storage needed (duh), more images in RAM at once, faster transfer to and from disc, fewer cycles wasted on waiting for I/O in general, the ability to store more intermediate datasets and more descriptive models, easier to archive the raw photo sets (which might be a big deal with all the legal issues popping up), etc. You want to cram a lot of data into memory, since the GPU will be performing lots of operations in parallel. Accessing the I/O bus must be one of the larger time sinks and CPU load becomes a concern just for moving data around.
I also wonder if the support for progressive loading might be useful for more efficient, low resolution variants of high resolution models. Just store one set of high res images and load them in progressive steps to make smaller data sets. Like, say you have a bunch of 8k images, but you only want to make a website banner based on the model from those 8k res images. I wonder if it’s possible to use the the progressive loading support to halt reading in the images at 1k. Lower resolution = less model data = smaller datasets to store or transfer. Basically skipping the downsampling.
Any time I see a big feature jump, like better file size, I assume the trade off in another feature negates at least half the benefit. It’s pretty rare, from what I’ve seen, to have improvements on all fronts.
Even better, this must be fantastic when you’re training AI models with millions of images. The compression level AND performance should be a game changer.
Ah, yes, you’re right! Thanks for that.
smh
That’s fucking tragic. Makes me want to whip out the ole Hacker Manifesto.
Kids will never again know the fun of dealing with long distance calling plans and the barely usable international calling that used to cost half you rent for a 15 minute conversation.
Probably based on the Cap’n Crunch whistle pay phone hack.
Someone correct me if I’ve missed a few bits, but here’s the story…
First, a little history.
Payphones were common. If you’re younger, you’ve probably seen them in movies. To operate them, you picked up the handset, listened for the dial tone (to make sure no one yanked the cord loose), inserted the amount shown by the coin slot, and then dialed. You have a limited amount of time before an automatic message would ask you to add more money. If you dialed a long distance number, a message would play telling you how much more you needed to insert.
There were no digital controls to this - no modern networking. The primitive “computers” were more like equipment you’d see in a science class. So, to deal with the transaction details, the coin slot mechanism would detect the type of coin inserted, mute the microphone on the handset, and transmit a series of tones. Just voltage spikes. The muting prevented the background noise from interfering with the signal detection. Drop a quarter in the slot and you’d hear the background noise suddenly disappear followed by some tapping sounds (this was just bleed through).
It’s also relevant to know that cereals used to include a cheap, little toy inside. At one point, Cap’n Crunch had a whistle which had a pitch of 2600Hz.
The story goes that someone* figured out that the tones sent by the payphones were at 2600Hz - same as the whistle. You could pick up a payphone handset and puff into the whistle a certain number of times, and ti would be detected as control signals (inserting money).
That’s right! Free phone calls to anywhere. I’m hazy on the specifics, but I’m pretty sure there were other tricks you could do, like directly calling restricted technician numbers, too. The reason the 2600Hz tone was special had to do with something like it was used as a general signal that didn’t trigger billing.
It knocked the idea of phone hacking, or “phreaking”, from a little known quirk, to an entire movement. Some of the stuff was wild and if you’re interested, look up the different “boxes” that people distributed blueprints for. Eventually, the phone companies caught on and started making it harder to get at wires and more sophisticated coin receptacles.
If you’ve ever seen the magazine 2600 back in the 90s and early 00s, that’s the origin of the name.
All that is to say, if you knew nothing about technology and watched a guy whistle into a phone to get special access, you’d probably be freaked out. Who knows what that maniac could do with a flute!
So you’re saying there are people who DO use “normies” and people that DON’T use “normies”. These are not two groups of people. Shit, I just joined this thread, so that makes ME one of YOU, and there’s OTHERS that aren’t here. Are WE the elitists? Or are THEY the “normies”? YOU said there’s no there’s no US or THEM, so EVERYONE is talking in this thread. ANYONE not in this thread must not exist because I know I exist, so YOU thread posters must exist, but wait, that makes ME an US and YOU a THEM.
(I’m not trying to be snarky, but this argument is exactly as nonsensical.)
Suuuure. Just like you totally have a girlfriend you met at summer camp in Canada. Bummer she’s not allowed to be near any electronics.
Only one of those two things can be real, and I’ve met a Canadian.
I see nothing fnord unusual about it.