H.264 Is Magic (2016) (sidbala.com)

https://en.wikipedia.org/wiki/Intel_Quick_Sync_Video

It took Nvidia until the encoder they bundled into 20xx series cards and Intel until the 6th or 7th generation of Quick Sync, but their hardware encoders are _finally_ at a point you can get decent quality at reasonable bitrates. Even so, x264 (and x265!) beat the pants off of them if one can spare the time and electricity; they're still that far ahead.

I think encode costs are higher (although I'm struggling to find any actual data) but in my experience this is totally negligible in practice.

Of course, don't go converting all your mp3s to Opus, but if you're running a music streaming server or whatever, Opus is pretty sweet... and probably close to "as good as it gets" until/unless we figure out some crazy new tricks.

In popular consciousness, maybe. When people think image they thing jpg. But most websites you use aren't delivering images as jpg. Most sites care about saving money on bandwidth and their site loading fast, and because of that they tend to use webp and avif, which are much smaller for the same quality.

mp3: Both AAC and opus have non-negligible gains and both are fairly widely used.

zip[1]: lz4, zstd, and lzma2 dominate at least gzip's implementation of deflate: For any given setting of gzip, there is a setting for at least one of those three that is both faster and smaller.

1: Not a codec; the zip format specifies a dozen methods for encoding. I'm assuming you mean Deflate, which is the most common compression method associated with zip.

It looks like they normally sell for around $200: https://www.ebay.com/sch/i.html?_nkw=NUC11TNKi3&LH_Sold=1

Definitely not something to be forgotten if you're expecting to compress lossless RGB, although this is pretty rare outside of niche-ish use cases (afaik game capture is basically the only place where people are likely to be using RGB and not YCbCr? could be wrong!)

I’m not sure if lossless h.264 is I only or not, but if it uses inter-frame compression then it’s difficult to randomly access arbitrary frames. For any arbitrary frame you’d have to find the nearest key frame, then interpolate from there based on the inter-frame compression metadata.

For lossy, neither is in a good place, since lossy AVIF beats both lossy JXL and lossy WEBP. Lossless AVIF is a joke and barely competes with PNG.

sudo apt install eom webp-pixbuf-loader libimlib2-dev libwebp-dev pkg-config libwebp-dev libgdk-pixbuf2.0-dev libgtk-3-dev

To build on the example used in the article, you could quantize an image by dropping the lower half of the bits of every pixel value. This discards half the information by mapping the larger input image space (e.g. 8 bits per pixel) to a smaller output image space (e.g. 4 bits per pixel), effectively scaling/rounding the original values.

The fact that higher frequencies can be quantized more intensely with relatively little image quality loss is a completely separate idea from quantization itself. In this context, high frequency masking as described in the article could be accurately described as a spatially dependent quantization to zero-bit output values. But the quantization part is the mapping, not the shape of the mask, and I think the article is misleading about the distinction.

In real codecs, there usually isn’t a hard boundary like a mask, but instead an empirical table of how many bits to keep based on the perceptual value of the information at each frequency.