Ultra-low bitrate image compression is a challenging and demanding topic. With the development of Large Multimodal Models (LMMs), a Cross Modality Compression (CMC) paradigm of Image-Text-Image has emerged. Compared with traditional codecs, this semantic-level compression can reduce image data size to 0.1\% or even lower, which has strong potential applications. However, CMC has certain defects in consistency with the original image and perceptual quality. To address this problem, we introduce CMC-Bench, a benchmark of the cooperative performance of Image-to-Text (I2T) and Text-to-Image (T2I) models for image compression. This benchmark covers 18,000 and 40,000 images respectively to verify 6 mainstream I2T and 12 T2I models, including 160,000 subjective preference scores annotated by human experts. At ultra-low bitrates, this paper proves that the combination of some I2T and T2I models has surpassed the most advanced visual signal codecs; meanwhile, it highlights where LMMs can be further optimized toward the compression task. We encourage LMM developers to participate in this test to promote the evolution of visual signal codec protocols.

翻译：超低码率图像压缩是一个具有挑战性且需求迫切的课题。随着大型多模态模型（LMMs）的发展，一种图像-文本-图像的跨模态压缩（CMC）范式应运而生。与传统编解码器相比，这种语义级压缩可将图像数据量缩减至0.1%甚至更低，具有强大的应用潜力。然而，CMC在图像还原一致性与感知质量方面仍存在一定缺陷。为解决该问题，我们提出了CMC-Bench——一个用于评估图像到文本（I2T）与文本到图像（T2I）模型在图像压缩任务中协同性能的基准测试。该基准分别涵盖18,000张与40,000张图像，用于验证6个主流I2T模型与12个T2I模型，并包含由专家标注的160,000个人类主观偏好评分。本文证实在超低码率下，部分I2T与T2I模型的组合性能已超越最先进的视觉信号编解码器；同时，研究也揭示了LMMs面向压缩任务可进一步优化的方向。我们鼓励LMM开发者参与此项测试，以推动视觉信号编解码协议的发展。