Why are camera taken videos at the same resolution way bigger than ones ripped from youtube?

Because they aren’t the same quality? Lossy compression is lossy

• https://m.youtube.com/watch?v=r6Rp-uo6HmI

• https://m.youtube.com/watch?v=h9j89L8eQQk

Because you can compress video without reducing the resolution, codec, or frame rate. When a camera records two green pixels: it records (green pixel) (green pixel). When the video is compressed, it changes to (two green pixels) which takes up less storage space but retains the same information. Compression is computationally expensive, which is why cameras typically don't do it on the fly.

A lot of the data in the video file you take isn't that visible but it's there for when you put it in editing software

For example: if you took that lower sized YouTube rip and threw it into editing software to tinker with the brightness (as a simple example) you quickly start getting artifacts whereas the video you took would be able to handle the editing without the artifacts much better as there's more data to work with

Basically the YouTube video has had all the extra data scrapped off that it doesn't need because it's not going to be edited, it's only going to be viewed

It' the individual frames that are compressed, essentially the video is unpacked and detail is culled from averages across multiple other frames beside it. So if the top of the video, for example the sky, doesn't change then that part will be kept static.

It's not so much properties about the video, but properties about each frame. I can take a 1080p image and blow it up to 8K in GIMP, but it's got the same detail as a 1080p image.

If you do multiple passes you can alleviate some of the downsides of low bitrates. You can always easily spot it in dark areas. I despise watching space movies or shows on streaming services because of the resulting excessive banding artifacts.

Video encoding has several tradeoffs:

• Bitrate
• Resolution/frame rate
• Perceived quality
• Computational complexity of encoding
• Computational complexity of decoding

The cell phone encoding chips for video encoding on device make sacrifices to preserve speed of encoding and preserve battery life (higher computational complexity costs more processing cycles and tends to use more power). So it's simpler encoding, in exchange for inefficient bitrate compression.

YouTube (and all the social media sites) have huge server farms with highly specialized encoding chips for making the videos more efficient with bitrate for quality. That makes sense because videos designed to be watched millions of times could benefit from even a very slight improvement of bitrate in exchange for a one-time cost of complex encoding. It's also why YouTube tends not to convert to AV1 (very efficient in bitrate for quality, but computationally complex to encode) until a video has a few hundred views, because it's not clear whether that tradeoff is worth it until they know a lot of people will be watching it.

Netflix customizes even further for a per-video basis and looks for even more specialized tricks on a scene-by-scene basis, because every single one of its videos only needs to be encoded once for each quality/format but will be watched millions of times.

In other words, it's like any other engineering problem. The engineers choose different tradeoffs based on context, which means that the cell phone applies a different set of tradeoffs compared to the social media site's server farm.