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New research advances learned image compression for human and machine perception · 3 sources tracked

By PulseAugur Editorial · [5 sources] ·

Three new research papers explore advancements in learned image compression. The first paper introduces a hierarchical latent representation with multiple scales to improve entropy modeling and achieve a 17.9% BD-rate reduction over VVC on Kodak datasets. The second paper addresses packet loss resilience by dispersing information across packets and using a dual-branch autoregressive structure, showing significant PSNR gains and reduced variance under packet loss. The third paper focuses on progressive learned image compression for machine perception, adapting a human-oriented codec with novel adapters and an adaptive decoding controller to maintain strong downstream classification performance. AI

IMPACT These advancements in learned image compression could lead to more efficient data transmission and storage, particularly in challenging network conditions, benefiting applications requiring high-fidelity image data for machine perception tasks.

RANK_REASON Cluster consists of three academic papers published on arXiv related to learned image compression techniques.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 5 sources. How we write summaries →

New research advances learned image compression for human and machine perception · 3 sources tracked

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COVERAGE [5]

  1. arXiv cs.AI TIER_1 English(EN) · Yuefeng Zhang ·

    HAMP-LIC: Hessian-Aware Mixed-Precision Post-Training Quantization for Learned Image Compression

    arXiv:2608.12239v1 Announce Type: cross Abstract: Use this plain-text version for the arXiv abstract field: Learned image compression (LIC) models achieve strong rate-distortion performance but are hindered by high computational complexity and encoding-decoding mismatches across …

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    HAMP-LIC: Hessian-Aware Mixed-Precision Post-Training Quantization for Learned Image Compression

    Use this plain-text version for the arXiv abstract field: Learned image compression (LIC) models achieve strong rate-distortion performance but are hindered by high computational complexity and encoding-decoding mismatches across heterogeneous hardware platforms. Uniform fixed-pr…

  3. arXiv cs.CV TIER_1 English(EN) · Jonas Brenig, Radu Timofte ·

    Multiple Scale Latents for Learned Image Compression

    arXiv:2608.10952v1 Announce Type: new Abstract: Most learned image compression systems rely on a single latent representation combined with a hyperprior, which limits their ability to efficiently capture image structure across spatial scales. In this work, we propose a hierarchic…

  4. arXiv cs.CV TIER_1 English(EN) · Yuhang Wei (Shanghai Jiao Tong University), Chuqin Zhou (Shanghai Jiao Tong University), Yibo Shi (Huawei Technologies Ltd), Jing Wang (Huawei Technologies Ltd), Guo Lu (Shanghai Jiao Tong University) ·

    Every Packet Counts: Dispersing Information for Loss-Resilient Learned Image Compression

    arXiv:2608.11096v1 Announce Type: new Abstract: Learned image compression (LIC) has achieved impressive rate-distortion performance. However, existing methods remain highly vulnerable to packet loss, a common challenge in satellite and emergency communications. This vulnerability…

  5. arXiv cs.CV TIER_1 English(EN) · Jungwoo Kim, Jun-Hyuk Kim, Jong-Seok Lee ·

    Progressive Learned Image Compression for Machine Perception

    arXiv:2512.20070v2 Announce Type: replace Abstract: Recent advances in learned image codecs have extended from human perception toward machine perception However, progressive image compression with fine granular scalability (FGS)-which enables decoding a single bitstream at multi…

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