Joint Degradation-Aware Arbitrary-Scale Super-Resolution for Variable-Rate Extreme Image Compression

Abstract

Recent diffusion-based extreme image compression methods have demonstrated remarkable performance at ultra-low bitrates. However, most approaches require training separate diffusion models for each target bitrate, resulting in substantial computational overhead and hindering practical deployment. Meanwhile, recent studies have shown that joint super-resolution can serve as an effective approach for enhancing low-bitrate reconstruction. However, when moving toward ultra-low bitrate regimes, these methods struggle due to severe information loss, and their reliance on fixed super-resolution scales prevents flexible adaptation across diverse bitrates. To address these limitations, we propose ASSR-EIC, a novel image compression framework that leverages arbitrary-scale super-resolution (ASSR) to support variable-rate extreme image compression (EIC). An arbitrary-scale downsampling module is introduced at the encoder side to provide controllable rate reduction, while a diffusion-based, joint degradation-aware ASSR decoder enables rate-adaptive reconstruction within a single model. We exploit the compression- and rescaling-aware diffusion prior to guide the reconstruction, yielding high fidelity and high realism restoration across diverse compression and rescaling settings. Specifically, we design a global compression-rescaling adaptor that offers holistic guidance for rate adaptation, and a local compression-rescaling modulator that dynamically balances generative and fidelity-oriented behaviors to achieve fine-grained, bitrate-adaptive detail restoration. To further enhance reconstruction quality, we introduce a dual semantic-enhanced design. Extensive experiments demonstrate that ASSR-EIC delivers state-of-the-art performance in extreme image compression while simultaneously supporting flexible bitrate control and adaptive rate-dependent reconstruction.

Figures (11)

• Figure 1: Framework overview of the proposed ASSR-EIC and its rate–distortion performance. Left: Framework overview, which integrates arbitrary-scale downsampling and diffusion-based joint degradation-aware ASSR reconstruction to achieve high-quality, bitrate-adaptive extreme image compression. Right: R–D (CLIPScore) curves on MSCOCO demonstrate that ASSR-EIC outperforms state-of-the-art extreme image compression methods PerCo careil2024towards and DiffEIC li2024towards, while supporting variable bitrates, illustrated using MS-ILLM (“quality 1”) as the anchor codec with different rescaling factors $s$.
• Figure 2: Structure overview of the proposed ASSR-EIC. We insert an arbitrary-scale downsampler to achieve controllable bitrate reduction at the encoder end, and propose a joint degradation-aware ASSR reconstruction decoder to restore the high-quality HR image with rate adaptation. The joint degradation-aware ASSR reconstruction decoder consists of a backbone, a fidelity module, a global compression-rescaling adaptor, and a local compression-rescaling modulator. The layers of the Backbone and the Fidelity Module are arranged from left to right to indicate the forward feature propagation order, with arrows omitted for clarity. $s, \chi_{CT}, \chi_{QP}$ denote the rescaling factor, codec type, and codec quality parameter, respectively.
• Figure 3: Details of the encoding embedding generation. We encode the float-type compression parameters and the rescaling factor into feature embeddings.
• Figure 4: Detailed structure of the global compression-rescaling adaptor.
• Figure 5: Detailed structure of the local compression-rescaling modulator.