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使用基于平均绝对误差最小化的上下文依赖线性预测的无损图像压缩
Grzegorz Ulacha1, Mirosław Łazoryszczak1
1Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, ul. Żołnierska 49, 71-210 Szczecin, Poland.
Entropy (Basel, Switzerland)
|January 8, 2025
概括
这项研究引入了一种新的无损图像压缩方法,具有快速解码. 它通过将模型调整为图像特征来实现更高的压缩效率,性能比JPEG-LS高9.1%.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 数据压缩数据压缩
背景情况:
- 无损图像压缩对于保持图像保真至关重要.
- 像JPEG-LS这样的现有方法提供了压缩,但可以提高效率和速度.
- 基于图像特征的自适应建模是提高压缩比的关键.
研究的目的:
- 开发一种具有快速解码的无损图像压缩方法.
- 通过调整编码器参数以适应图像内容来提高压缩效率.
- 根据已建立的压缩标准来评估拟议的方法.
主要方法:
- 使用线性和非线性预测进行数据建模,并通过上下文依赖的常量组件删除块进行增强.
- 通过双阶段压缩编码预测错误:自适应性戈伦布和二进制算术编码.
- 实现一个作者的上下文切换算法,用于跨图像区域量身定制的预测模型.
主要成果:
- 通过将预测模型适应特定图像区域,实现了高压缩效率.
- 与JPEG-LS相比,在一个测试基础上,平均文件比特率提高了9.1%.
- 分析了个别编码器参数,压缩效率和编码时间之间的权衡.
结论:
- 拟议的无损图像压缩方法提供了卓越的效率和快速解码.
- 语境交换算法通过将模型定制为图像数据,显著改善了压缩.
- 该方法为现有的无损图像压缩技术提供了有竞争力的替代方案.
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