CMC-Bench: Towards a New Paradigm of Visual Signal Compression
<|reference_start|>CMC-Bench: Towards a New Paradigm of Visual Signal Compression: 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.<|reference_end|>
@article{li2024cmc-bench:,
    title={CMC-Bench: Towards a New Paradigm of Visual Signal Compression},
    author={Chunyi Li, Xiele Wu, Haoning Wu, Donghui Feng, Zicheng Zhang, Guo Lu,
  Xiongkuo Min, Xiaohong Liu, Guangtao Zhai, Weisi Lin},
    journal={arXiv preprint arXiv:2406.09356},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09356},
    primaryClass={cs.CV eess.IV}
}