PerCoV2: Improved Ultra-Low Bit-Rate Perceptual Image Compression with
  Implicit Hierarchical Masked Image Modeling
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.
67d2ca4be4696fda20bac029
Nikolai Körber
claimed_verified
2025-03-14T08:57:39.634Z
656c8721e8bf55919a9732c5
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/bvIaJLEvlWal1PHCL4tDo.jpeg
Nikolai
user
Nikolai10
67d2ca4be4696fda20bac02a
Eduard Kromer
admin_assigned
2025-03-14T11:15:08.897Z
62f7f00647d782a6e28661df
/avatars/d2e5313995e14109c1dedc3a67d67707.svg
Eduard Kromer
user
edukrom
67d2ca4be4696fda20bac02b
Andreas Siebert
admin_assigned
2025-03-14T11:15:28.060Z
64e48bd69ec28a2b5a016fbd
/avatars/c430012488f806579d4b5a4eab9c55b5.svg
Andreas Siebert
user
HerrSiebert
67d2ca4be4696fda20bac02c
Sascha Hauke
67d2ca4be4696fda20bac02d
Daniel Mueller-Gritschneder
67d2ca4be4696fda20bac02e
Björn Schuller
PerCoV2: Improved Ultra-Low Bit-Rate Perceptual Image Compression with
  Implicit Hierarchical Masked Image Modeling
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.
PerCoV2, an ultra-low bit-rate perceptual image compression system, achieves higher image fidelity and lower bit-rates using Stable Diffusion 3 and advanced entropy coding methods.
Stable Diffusion 3 entropy coding discrete hyper-latent image distribution autoregressive methods VAR MaskGIT MSCOCO-30k benchmark hybrid generation mode