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.

超低比特率图像压缩是一个具有挑战性和需求量大的主题。这篇论文介绍了一个基于图像 - 文本 - 图像的语义级压缩方法，通过验证不同模型的协同性能，证明了在超低比特率下一些模型的组合已超过最先进的视觉信号编解码器，同时强调了在压缩任务中进一步优化大规模多模态模型的方向。