PerCoV2: Improved Ultra-Low Bit-Rate Perceptual Image Compression with
  Implicit Hierarchical Masked Image Modeling
Authors: name: Nikolai Körber, name: Eduard Kromer, name: Andreas Siebert, name: Sascha Hauke, name: Daniel Mueller-Gritschneder, name: Björn Schuller

We introduce PerCoV2, a novel and open ultra-low bit-rate perceptual image
compression system designed for bandwidth- and storage-constrained
applications. Building upon prior work by Careil et al., PerCoV2 extends the
original formulation to the Stable Diffusion 3 ecosystem and enhances entropy
coding efficiency by explicitly modeling the discrete hyper-latent image
distribution. To this end, we conduct a comprehensive comparison of recent
autoregressive methods (VAR and MaskGIT) for entropy modeling and evaluate our
approach on the large-scale MSCOCO-30k benchmark. Compared to previous work,
PerCoV2 (i) achieves higher image fidelity at even lower bit-rates while
maintaining competitive perceptual quality, (ii) features a hybrid generation
mode for further bit-rate savings, and (iii) is built solely on public
components. Code and trained models will be released at
https://github.com/Nikolai10/PerCoV2.