CMC-Bench: Towards a New Paradigm of Visual Signal Compression
This addresses the problem of semantic-level image compression for applications requiring minimal data size, though it is incremental in benchmarking existing models.
The paper tackles the challenge of ultra-low bitrate image compression by introducing CMC-Bench, a benchmark for evaluating Cross Modality Compression (CMC) using Image-to-Text and Text-to-Image models, showing that some combinations surpass state-of-the-art codecs at bitrates as low as 0.1%.
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.