图像压缩技术:经典和"基于兴趣区域"的方法,在最近的论文中提出
Vlad-Ilie Ungureanu1, Paul Negirla1, Adrian Korodi1
1Automation and Applied Informatics Department, University Politehnica Timisoara, 300006 Timisoara, Romania.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
本研究回顾了图像压缩方法,重点关注感兴趣区域 (ROI) 选择和混合技术. 它根据压缩比和质量指标对经典和新的方法进行分类,以在资源稀缺的领域有效处理数据.
科学领域:
- 计算机科学 计算机科学
- 图像处理 图像处理
- 数据压缩数据压缩
背景情况:
- 图像压缩对于汽车和远程医疗等资源有限的领域至关重要.
- 有效的存储,传输和解压对于实时系统至关重要.
- 感兴趣区域 (ROI) 压缩技术保留了关键的图像区域,同时减少了数据大小.
研究的目的:
- 审查和分析过去十年关于ROI选择和压缩技术的相关文献.
- 通过将它们与经典方法进行比较来突出混合压缩方法的新性.
- 根据性能指标,提供对压缩方法的分类概述.
主要方法:
- 专注于ROI选择和图像压缩的论文的系统文献审查.
- 分析经典和混合压缩技术.
- 基于压缩比,平均平方误差 (MSE),峰值信号噪声比 (PSNR) 和结构相似度指数 (SSIM) 的方法的分类.
主要成果:
- 确定了一系列经典和混合图像压缩技术.
- 基于关键绩效指标 (KPIs) 的分类方法,如压缩比和质量指标.
- 强调了混合方法在平衡压缩效率和图像质量的有效性.
结论:
- 该研究提供了一个全面的概述,以指导研究人员选择特定领域的适当压缩算法.
- 了解压缩比和质量指标等性能参数对于优化图像压缩至关重要.
- 混合压缩方法在各种应用中显示出对高效图像处理的承诺.
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