Recent progress in generative compression technology has significantly
improved the perceptual quality of compressed data. However, these advancements
primarily focus on producing high-frequency details, often overlooking the
ability of generative models to capture the prior distribution of image
content, thus impeding further bitrate reduction in extreme compression
scenarios (<0.05 bpp). Motivated by the capabilities of predictive language
models for lossless compression, this paper introduces a novel Unified Image
Generation-Compression (UIGC) paradigm, merging the processes of generation and
compression. A key feature of the UIGC framework is the adoption of
vector-quantized (VQ) image models for tokenization, alongside a multi-stage
transformer designed to exploit spatial contextual information for modeling the
prior distribution. As such, the dual-purpose framework effectively utilizes
the learned prior for entropy estimation and assists in the regeneration of
lost tokens. Extensive experiments demonstrate the superiority of the proposed
UIGC framework over existing codecs in perceptual quality and human perception,
particularly in ultra-low bitrate scenarios (<=0.03 bpp), pioneering a new
direction in generative compression.

本文介绍了一种新的统一图像生成 - 压缩（UIGC）范式，该范式将生成和压缩过程合并在一起，通过采用矢量量化图像模型和多阶段 Transformer 来利用空间上下文信息建模先验分布，从而在实现熵估计和丢失令牌再生成方面成功使用学习到的先验，实验结果表明该 UIGC 框架在感知质量和人类感知方面优于现有编解码器，在极低比特率场景下（<=0.03 bpp）开创了生成式压缩的新方向。