High Efficiency Image Compression for Large Visual-Language Models

• URL: http://arxiv.org/abs/2407.17060v1
• Date: Wed, 24 Jul 2024 07:37:12 GMT
• Title: High Efficiency Image Compression for Large Visual-Language Models
• Authors: Binzhe Li, Shurun Wang, Shiqi Wang, Yan Ye,
• Abstract summary: Large visual language models (LVLMs) have shown impressive performance and promising generalization capability in multi-modal tasks. We propose a variable image compression framework consisting of a pre-editing module and an end-to-end to achieve promising rate-accuracy performance.
• Score: 14.484831372497437
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, large visual language models (LVLMs) have shown impressive performance and promising generalization capability in multi-modal tasks, thus replacing humans as receivers of visual information in various application scenarios. In this paper, we pioneer to propose a variable bitrate image compression framework consisting of a pre-editing module and an end-to-end codec to achieve promising rate-accuracy performance for different LVLMs. In particular, instead of optimizing an adaptive pre-editing network towards a particular task or several representative tasks, we propose a new optimization strategy tailored for LVLMs, which is designed based on the representation and discrimination capability with token-level distortion and rank. The pre-editing module and the variable bitrate end-to-end image codec are jointly trained by the losses based on semantic tokens of the large model, which introduce enhanced generalization capability for various data and tasks. {Experimental results demonstrate that the proposed framework could efficiently achieve much better rate-accuracy performance compared to the state-of-the-art coding standard, Versatile Video Coding.} Meanwhile, experiments with multi-modal tasks have revealed the robustness and generalization capability of the proposed framework.

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