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arXiv · 2506.16102

Fast Training-free Perceptual Image Compression

Ziran Zhu·Tongda Xu·Minye Huang·Dailan He·Xingtong Ge·Xinjie Zhang·Ling Li·Yan Wang
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Abstract

Training-free perceptual image codec adopt pre-trained unconditional generative model during decoding to avoid training new conditional generative model. However, they heavily rely on diffusion inversion or sample communication, which take 1 min to intractable amount of time to decode a single image. In this paper, we propose a training-free algorithm that improves the perceptual quality of any existing codec with theoretical guarantee. We further propose different implementations for optimal perceptual quality when decoding time budget is $\approx 0.1$s, $0.1-10$s and $\ge 10$s. Our approach: 1). improves the decoding time of training-free codec from 1 min to $0.1-10$s with comparable perceptual quality. 2). can be applied to non-differentiable codec such as VTM. 3). can be used to improve previous perceptual codecs, such as MS-ILLM. 4). can easily achieve perception-distortion trade-off. Empirically, we show that our approach successfully improves the perceptual quality of ELIC, VTM and MS-ILLM with fast decoding. Our approach achieves comparable FID to previous training-free codec with significantly less decoding time. And our approach still outperforms previous conditional generative model based codecs such as HiFiC and MS-ILLM in terms of FID. The source code is provided in the supplementary material.

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Ziran Zhu, Tongda Xu, Minye Huang, Dailan He, Xingtong Ge, Xinjie Zhang, Ling Li, Yan Wang. 2025-06-19. Fast Training-free Perceptual Image Compression. https://arxiv.org/abs/2506.16102

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