  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.
