Extremely low-bitrate Image Compression Semantically Disentangled by LMMs from a Human Perception Perspective

• URL: http://arxiv.org/abs/2503.00399v3
• Date: Sat, 12 Apr 2025 11:05:12 GMT
• Title: Extremely low-bitrate Image Compression Semantically Disentangled by LMMs from a Human Perception Perspective
• Authors: Juan Song, Lijie Yang, Mingtao Feng,
• Abstract summary: Inspired by human progressive perception mechanism, we propose a Semantically Disentangled Image Compression framework.<n>We leverage LMMs to extract essential semantic components, including overall descriptions, object detailed description, and semantic segmentation masks.<n>We propose a training-free Object Restoration model with Attention Guidance (ORAG) built on pre-trained ControlNet to restore object details conditioned by object-level text descriptions and semantic masks.
• Score: 2.542077227403488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It remains a significant challenge to compress images at extremely low bitrate while achieving both semantic consistency and high perceptual quality. Inspired by human progressive perception mechanism, we propose a Semantically Disentangled Image Compression framework (SEDIC) in this paper. Initially, an extremely compressed reference image is obtained through a learned image encoder. Then we leverage LMMs to extract essential semantic components, including overall descriptions, object detailed description, and semantic segmentation masks. We propose a training-free Object Restoration model with Attention Guidance (ORAG) built on pre-trained ControlNet to restore object details conditioned by object-level text descriptions and semantic masks. Based on the proposed ORAG, we design a multistage semantic image decoder to progressively restore the details object by object, starting from the extremely compressed reference image, ultimately generating high-quality and high-fidelity reconstructions. Experimental results demonstrate that SEDIC significantly outperforms state-of-the-art approaches, achieving superior perceptual quality and semantic consistency at extremely low-bitrates ($\le$ 0.05 bpp).

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