title: PerCoV2: Improved Ultra-Low Bit-Rate Perceptual Image Compression with
    Implicit Hierarchical Masked Image Modeling
summary: 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.
paper_authors._id: 67d2ca4be4696fda20bac029
paper_authors.name: Nikolai Körber
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-14T08:57:39.634Z
paper_authors._id: 656c8721e8bf55919a9732c5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/bvIaJLEvlWal1PHCL4tDo.jpeg
paper_authors.fullname: Nikolai
paper_authors.type: user
paper_authors.user: Nikolai10
paper_authors._id: 67d2ca4be4696fda20bac02a
paper_authors.name: Eduard Kromer
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T11:15:08.897Z
paper_authors._id: 62f7f00647d782a6e28661df
paper_authors.avatarUrl: /avatars/d2e5313995e14109c1dedc3a67d67707.svg
paper_authors.fullname: Eduard Kromer
paper_authors.type: user
paper_authors.user: edukrom
paper_authors._id: 67d2ca4be4696fda20bac02b
paper_authors.name: Andreas Siebert
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T11:15:28.060Z
paper_authors._id: 64e48bd69ec28a2b5a016fbd
paper_authors.avatarUrl: /avatars/c430012488f806579d4b5a4eab9c55b5.svg
paper_authors.fullname: Andreas Siebert
paper_authors.type: user
paper_authors.user: HerrSiebert
paper_authors._id: 67d2ca4be4696fda20bac02c
paper_authors.name: Sascha Hauke
paper_authors._id: 67d2ca4be4696fda20bac02d
paper_authors.name: Daniel Mueller-Gritschneder
paper_authors._id: 67d2ca4be4696fda20bac02e
paper_authors.name: Björn Schuller
paper_title: PerCoV2: Improved Ultra-Low Bit-Rate Perceptual Image Compression with
    Implicit Hierarchical Masked Image Modeling
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Stable Diffusion 3 entropy coding discrete hyper-latent image distribution autoregressive methods VAR MaskGIT MSCOCO-30k benchmark hybrid generation mode