Comment by xrd | Hacker News Reader

Comment by xrd | Hacker News Reader

It's interesting seeing how camera support is the biggest gaping hole in the open source smart phone world.

For example, almost everything in this graph is green, except for the camera column:

https://wiki.postmarketos.org/wiki/Devices

It's a complicated problem:

https://blog.brixit.nl/pinephone-camera-part-2/

But, in a world where open source almost always exceeds the commercial offerings, I'm surprised this is the case. It seems like the parent article and the LWN article posted (https://lwn.net/Articles/904776/) are suggesting that the trend is to just dump raw data and let software process it. I'm doubly surprised there aren't good libraries in the open source world that do this so much better than the commercial ones.

This is the main reason I feel stuck inside the duopoly of Android (barely tolerable and hostile) and iOS (completely unusable and unhackable). I need a good camera on my phone and there aren't good options for that at all.

Maybe extracting this code outside of gstreamer is a good idea if this takes us closer to that goal.

Your post is only partially correct. Even with completely open firmware, or at least a documented interface, the biggest roadblock would become be abysmal state of OSS digital image processing. Engineers at major smartphone and camera companies are paid top dollar to improve how the images are processed, this is no laughing matter and is obvious when you compare the image quality from a no-name chinese brand with Samsung or Apple even though they are using the same exact sensors.

FOSS smartphones such as the Pinephone would then need a whole bunch of accelerators to perform such computations because the general purpose CPU would be too slow for that, and image could take seconds to finish processing and get saved in the gallery. But at that point Pinephone itself would not have enough expertise for such a design and everything would crumble.

... to add to your point, then take companies like Google who rely heavily on ML to improve the quality of the photos further and the distance between "RAW data off sensor" and "the best Samsung/Apple/Google can generate" is a HUGE gap.

> is obvious when you compare the image quality from a no-name chinese brand with Samsung or Apple even though they are using the same exact sensors.

This is an area where FLOSS has an opportunity to shine. I think many of these algorithms are described in scientific papers and considering FLOSS is much more collaboration-prone, I'd really expect the best algorithms (except for the ones that require much training data) to soon be implemented. An example of a success case: AV1.

and who's designing and manufacturing and programming the accelerators needed for those algorithms to perform close to real time?

In the case of AV1, many of the members of the consortium.

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