Abstract
Ultra-low bitrate image compression (below 0.05 bits per pixel) is increasingly critical for bandwidth-constrained and computation-limited encoding scenarios such as edge devices. Existing frameworks typically rely on large pretrained encoders (e.g., VAEs or tokenizer-based models) and perform transform coding within their generative latent space. While these approaches achieve impressive perceptual fidelity, their reliance on heavy encoder networks makes them unsuitable for deployment on weak sender devices. In this work, we explore the feasibility of applying shallow encoders for ultra-low bitrate compression and propose a novel Asymmetric Extreme Image Compression (AEIC) framework that pursues simultaneously encoding simplicity and decoding quality. Specifically, AEIC employs moderate or even shallow encoder networks, while leveraging an one-step diffusion decoder to maintain high-fidelity and high-realism reconstructions under extreme bitrates. To further enhance the efficiency of shallow encoders, we design a dual-side feature distillation scheme that transfers knowledge from AEIC with moderate encoders to its shallow encoder variants. Experiments demonstrate that AEIC not only outperforms existing methods on rate-distortion-perception performance at ultra-low bitrates, but also delivers exceptional encoding efficiency for 35.8 FPS on 1080P input images, while maintaining competitive decoding speed compared to existing methods.
Abstract (translated)
超低比特率图像压缩（低于每像素0.05位）在带宽受限和计算资源有限的编码场景中，如边缘设备上变得越来越重要。现有的框架通常依赖于大型预训练编码器（例如，变分自编码器VAEs或基于标记化的方法），并在其生成潜在空间内执行变换编码。尽管这些方法实现了令人印象深刻的感知保真度，但它们对重型编码网络的依赖使它们不适合部署在计算能力较弱的设备上。 在这项工作中，我们探讨了应用浅层编码器进行超低比特率压缩的可能性，并提出了一个新颖的非对称极端图像压缩（AEIC）框架，旨在同时追求编码简洁性和解码质量。具体而言，AEIC采用中等深度或甚至浅层的编码网络，同时利用一步扩散解码器，在极低比特率下保持高保真度和高度真实的重建效果。 为了进一步提高浅层编码器的效率，我们设计了一种双侧特征蒸馏方案，将知识从具有中等编码网络的AEIC转移到其浅层编码变体上。实验表明，AEIC不仅在超低比特率下的速率失真感知性能方面优于现有方法，而且在1080P输入图像的情况下实现了35.8 FPS的出色编码效率，并且解码速度与现有方法相比具有竞争力。
URL
https://arxiv.org/abs/2512.12229
