Abstract
While recent neural codecs achieve strong performance at low bitrates when optimized for perceptual quality, their effectiveness deteriorates significantly under ultra-low bitrate conditions. To mitigate this, generative compression methods leveraging semantic priors from pretrained models have emerged as a promising paradigm. However, existing approaches are fundamentally constrained by a tradeoff between semantic faithfulness and perceptual realism. Methods based on explicit representations preserve content structure but often lack fine-grained textures, whereas implicit methods can synthesize visually plausible details at the cost of semantic drift. In this work, we propose a unified framework that bridges this gap by coherently integrating explicit and implicit representations in a training-free manner. Specifically, We condition a diffusion model on explicit high-level semantics while employing reverse-channel coding to implicitly convey fine-grained details. Moreover, we introduce a plug-in encoder that enables flexible control of the distortion-perception tradeoff by modulating the implicit information. Extensive experiments demonstrate that the proposed framework achieves state-of-the-art rate-perception performance, outperforming existing methods and surpassing DiffC by 29.92%, 19.33%, and 20.89% in DISTS BD-Rate on the Kodak, DIV2K, and CLIC2020 datasets, respectively.
Abstract (translated)
虽然近期的神经编码器在优化感知质量时，在低比特率条件下表现出色，但在极低比特率条件下的效果显著下降。为了缓解这一问题，生成式压缩方法利用预训练模型中的语义先验，作为一种有前景的方法出现。然而，现有方法从根本上受到语义忠实性和感知逼真性之间的权衡制约：基于显式表示的方法可以保持内容结构，但往往缺乏精细纹理；而隐式方法虽然能够合成视觉上合理的细节，但却可能以牺牲语义一致性为代价。 在本工作中，我们提出了一种统一框架，通过训练自由的方式将显式和隐式表示一致地整合起来，从而弥合了这一差距。具体而言，我们在扩散模型中使用显式的高层语义进行条件化设置，并采用反向通道编码来隐式传达精细细节。此外，我们引入了一个插件式编码器，可以通过调节隐式信息灵活控制失真-感知权衡。 广泛的实验表明，所提出的框架在率-感知性能方面达到了最先进的水平，优于现有的方法，在Kodak、DIV2K和CLIC2020数据集上分别比DiffC高出29.92%、19.33% 和 20.89%的DISTS BD-Rate。
URL
https://arxiv.org/abs/2602.05213
