有效的视频压缩使用后图像表示
Minseong Jeon1, Kyungjoo Cheoi1
1Department of Computer Science, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju 28644, Chungbuk, Republic of Korea.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
这项研究介绍了一种以后图像为基础的视频压缩方法,可以大大减少95.97%的数据大小,同时保持分析性能. 这种技术允许大型语言模型 (LLM) 从压缩后图片解释视频内容.
科学领域:
- 计算机视觉 计算机视觉
- 数据压缩数据压缩
- 人工智能的人工智能
背景情况:
- 大规模的视频数据需要高效的压缩来改进处理.
- 现有的方法可能难以平衡压缩比与分析性能保护.
研究的目的:
- 提出和评估基于后图像的视频压缩方法.
- 为了减少视频数据量,同时保持或提高分析性能.
- 通过大型语言模型 (LLM) 评估压缩数据的可解释性.
主要方法:
- 根据场景复杂度,使用光流进行自适应的关键选择.
- 通过对象运动面具的时间积累通过alpha混合生成后图像.
- 使用UCF-Crime数据集进行压缩比和分类任务 (二进制和多类) 的评估.
主要成果:
- 在UCF-Crime数据集上实现了95.97%的压缩比.
- 压缩视频在二进制分类中保持了可比的性能,在多类分类中表现优于原始视频.
- 在异常行为分类中表现出显著的4.25%的性能改善.
- 已确认的LLM可以从单个后图片中解释时间上下文.
结论:
- 以后图像为基础的压缩有效地保留了时空信息,并显著减少了数据大小.
- 该方法为高效的视频数据管理和分析提供了可行的解决方案.
- 压缩的视频数据保留了足够的信息来进行先进的AI解释.
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