Dual-Representation Image Compression at Ultra-Low Bitrates via Explicit Semantics and Implicit Textures

TL;DR

This paper introduces a unified, training-free image compression framework that combines explicit semantic representations with implicit texture synthesis, achieving state-of-the-art ultra-low bitrate performance.

Contribution

It presents a novel method integrating explicit and implicit representations via a diffusion model and reverse-channel coding, enabling flexible control over compression quality and perceptual realism.

Findings

Outperforms existing methods in rate-perception metrics.

Surpasses DiffC by approximately 20-30% in DISTS BD-Rate.

Achieves state-of-the-art results on multiple datasets.

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.