CMC-Bench: Towards a New Paradigm of Visual Signal Compression

• URL: http://arxiv.org/abs/2406.09356v1
• Date: Thu, 13 Jun 2024 17:41:37 GMT
• Title: CMC-Bench: Towards a New Paradigm of Visual Signal Compression
• Authors: Chunyi Li, Xiele Wu, Haoning Wu, Donghui Feng, Zicheng Zhang, Guo Lu, Xiongkuo Min, Xiaohong Liu, Guangtao Zhai, Weisi Lin,
• Abstract summary: We introduce CMC-Bench, a benchmark of the cooperative performance of Image-to-Text (I2T) and Text-to-Image (T2I) models for image compression. At ultra-lows, this paper proves that the combination of some I2T and T2I models has surpassed the most advanced visual signal protocols.
• Score: 85.1839779884282
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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