currentscurrents t1_jc5ghbv wrote on March 14, 2023 at 4:18 AM

Reply to comment by Red-Portal in [D]: Generalisation ability of autoencoders by Blutorangensaft

Would love to read some research papers if you have a link!

But I mean that we do not use them for compression in practice. We use hand-crafted algorithms, mostly DEFLATE for lossless + a handful of lossy DCT-based algorithms for audio/video/images.

Red-Portal t1_jc5gmb0 wrote on March 14, 2023 at 4:19 AM

Can't remember those on compression, but for quantization I was talking about this paper

FrogBearSalamander t1_jc5vvrb wrote on March 14, 2023 at 7:24 AM

> Would love to read some research papers if you have a link!

Nonlinear Transform Coding
An Introduction to Neural Data Compression
SoundStream: An End-to-End Neural Audio Codec
Old but foundational: End-to-end Optimized Image Compression
And this paper made the connection between compression models and VAEs: Variational image compression with a scale hyperprior
Any VQ-based model (VQ-VAE, VQ-GAN, etc.) can be interpreted as a compression model. Many generative image models use VQ but they don't often present rate-distortion results. And, as /u/speyside42 said above, they typically assume a uniform distribution over the codebook, which isn't very interesting from a compression point of view. Instead, you want to learn a distribution and use it as an entropy model in conjunction with an entropy coder. Note that SoundStream (mentioned above) uses residual VQ (RVQ).
Image Compression with Product Quantized Masked Image Modeling uses a kind of VQ (subdivide the latent vectors and code separate to form a product quantizer) along with masked image modeling (MIM) to get a conditional distribution over codewords. MIM is often used for generation but here they entropy code instead of sample.