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

2026-02-05

AI Summary

Existing neural image compression methods struggle to simultaneously preserve semantic fidelity and perceptual realism at ultra-low bitrates: explicit representations often lack textural detail, while implicit ones are prone to semantic drift. This work proposes the first training-free unified framework that synergistically optimizes semantics and perception by guiding a diffusion model with explicit high-order semantic information and implicitly conveying fine-grained textures via reverse channel coding. The approach innovatively integrates explicit semantic and implicit texture representations and introduces a plug-in encoder to flexibly balance the distortion-perception trade-off. Evaluated on Kodak, DIV2K, and CLIC2020, the method achieves state-of-the-art perceptual performance, surpassing DiffC by 29.92%, 19.33%, and 20.89% in DISTS BD-Rate, respectively.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated 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.

Problem

• ultra-low bitrates
• semantic faithfulness
• perceptual realism
• image compression
• explicit-implicit representation

Innovation

• dual-representation
• ultra-low bitrate compression
• explicit semantics
• implicit textures
• reverse-channel coding