Technology

Honest Limitations Though

TurboQuant was designed for attention score accuracy, not perceptual image quality — PSNR and SSIM care about different things than transformer attention distributions The "zero accuracy loss" claim is specific to its use case; adapted to video you'd need to re-validate quality metrics Motion estimation (the biggest win in video codecs) isn't addressed at all by TurboQuant — you'd still need that separately Real-time decoding constraints are much stricter in video than in LLM inference

Bottom Line It probably wouldn't replace a traditional codec on its own, but the PolarQuant + QJL two-stage approach applied to neural codec latents is a genuinely novel research direction that doesn't seem to have been explored yet. If you combined it with solid motion estimation, you could have something interesting — especially for scenarios where you want very aggressive compression (3-4 bits) with provable quality bounds.