无需训练即可快速提升图像压缩感知质量，解码快10倍以上。
Fast Training-free Perceptual Image Compression
- 利用预训练生成模型加速解码，避免复杂迭代优化。
- 0.1-10秒内完成解码，FID表现接近顶尖方法。
- 兼容非可微编码器，适合快速部署与效果增强。
无训练感知图像编码器在解码时采用预训练的无条件生成模型，避免训练新的条件生成模型。然而，它们严重依赖扩散反演或样本通信，导致单张图像解码时间长达1分钟，难以实用。本文提出一种无需训练的算法，可在理论保证下提升任意现有编码器的感知质量。针对不同解码时间预算（约0.1秒、0.1–10秒、≥10秒），设计了多版本实现方案。本方法将无训练编码器的解码时间从1分钟缩短至0.1–10秒，同时保持相当的感知质量；可应用于不可微编码器如VTM；能提升如MS-ILLM等已有感知编码器性能；且易于实现感知-失真权衡。实验表明，该方法显著提升了ELIC、VTM和MS-ILLM的感知质量，解码速度大幅提升，其FID表现接近先前无训练方法，并优于基于条件生成模型的HiFiC和MS-ILLM。
原文摘要 · Abstract (English)
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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