Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaption

• URL: http://arxiv.org/abs/2406.00758v3
• Date: Wed, 04 Dec 2024 09:36:56 GMT
• Title: Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaption
• Authors: Anqi Li, Feng Li, Yuxi Liu, Runmin Cong, Yao Zhao, Huihui Bai,
• Abstract summary: This paper proposes a Control Generative Image Compression framework, termed Control-GIC. Control-GIC is capable of fine-grained adaption across a broad spectrum while ensuring high-fidelity and generality compression. We develop a conditional decoder capable of retrieving historic multi-granularity representations according to encoded codes, and then reconstruct hierarchical features in the formalization of conditional probability.
• Score: 52.82508784748278
• License:
• Abstract: Although recent generative image compression methods have demonstrated impressive potential in optimizing the rate-distortion-perception trade-off, they still face the critical challenge of flexible rate adaption to diverse compression necessities and scenarios. To overcome this challenge, this paper proposes a Controllable Generative Image Compression framework, termed Control-GIC, the first capable of fine-grained bitrate adaption across a broad spectrum while ensuring high-fidelity and generality compression. Control-GIC is grounded in a VQGAN framework that encodes an image as a sequence of variable-length codes (i.e. VQ-indices), which can be losslessly compressed and exhibits a direct positive correlation with the bitrates. Drawing inspiration from the classical coding principle, we correlate the information density of local image patches with their granular representations. Hence, we can flexibly determine a proper allocation of granularity for the patches to achieve dynamic adjustment for VQ-indices, resulting in desirable compression rates. We further develop a probabilistic conditional decoder capable of retrieving historic encoded multi-granularity representations according to transmitted codes, and then reconstruct hierarchical granular features in the formalization of conditional probability, enabling more informative aggregation to improve reconstruction realism. Our experiments show that Control-GIC allows highly flexible and controllable bitrate adaption where the results demonstrate its superior performance over recent state-of-the-art methods.

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