← 研踪 PaperTrailarXiv:2503.11321 · v2
Fractal-Informed Spatial-Frequency Adaptation for Texture Generation in Natural Image Compression
首次提交 2025-03-14 19:41
版本更新 2026-11-12 01:31
雷达观测 2026-11-13 14:16
摘要
Generative image compression aims to reconstruct realistic images at low bitrates by balancing the trade-off among rate, distortion, and realism. However, recovering plausible textures while preserving reconstruction fidelity remains challenging, particularly under limited bandwidth. In this paper, we propose a diffusion-informed generative image compression framework that exploits frequency-domain representations and diffusion knowledge to improve texture reconstruction. Specifically, we approach the rate-distortion-realism trade-off from three complementary perspectives. First, motivated by the close relationship between image textures and frequency characteristics, we develop a Fractal Frequency-Aware Band Image Compression (FFAB-IC) network to capture directional frequency components in natural images. By integrating fractal band operations into nonlinear neural transformations, the network preserves essential image information while suppressing redundant details. Second, we incorporate diffusion knowledge into the encoder and introduce iterative diffusion refinement into the decoder, enabling the recovery of realistic textures under constrained bitrates. Third, we introduce frequency- and content-aware regularization to jointly exploit spatial structures and frequency characteristics during optimization. Extensive quantitative and qualitative experiments demonstrate that our method achieves a superior rate-distortion-realism trade-off, delivering higher realism at comparable distortion than existing approaches.
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v2 · 2026-11-12 01:31 · 最新观测版本
Fractal-Informed Spatial-Frequency Adaptation for Texture Generation in Natural Image Compression
Generative image compression aims to reconstruct realistic images at low bitrates by balancing the trade-off among rate, distortion, and realism. However, recovering plausible textures while preserving reconstruction fidelity remains challenging, particularly under limited bandwidth. In this paper, we propose a diffusion-informed generative image compression framework that exploits frequency-domain representations and diffusion knowledge to improve texture reconstruction. Specifically, we approach the rate-distortion-realism trade-off from three complementary perspectives. First, motivated by the close relationship between image textures and frequency characteristics, we develop a Fractal Frequency-Aware Band Image Compression (FFAB-IC) network to capture directional frequency components in natural images. By integrating fractal band operations into nonlinear neural transformations, the network preserves essential image information while suppressing redundant details. Second, we incorporate diffusion knowledge into the encoder and introduce iterative diffusion refinement into the decoder, enabling the recovery of realistic textures under constrained bitrates. Third, we introduce frequency- and content-aware regularization to jointly exploit spatial structures and frequency characteristics during optimization. Extensive quantitative and qualitative experiments demonstrate that our method achieves a superior rate-distortion-realism trade-off, delivering higher realism at comparable distortion than existing approaches.
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