使用变压器和残余网络进行有效的图像压缩,以平衡处理高频和低频信息
Jianhua Hu1, Guixiang Luo2, Xiangfei Feng1
1Computer Engineering Technical College, Guangdong Polytechnic of Science and Technology, Zhuhai, Guangdong, China.
PloS one
|October 3, 2025
概括
一个新的变压器和残余网络 (TRN) 通过有效捕获高频和低频细节来改善图像压缩. 这种深度学习方法显著优于现有方法,提供更好的比特率扭曲性能.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 图像处理 图像处理
背景情况:
- 深度学习,特别是变压器网络,通过捕获高频特征来进行先进的图像压缩.
- 变压器网络努力有效处理低频图像信息.
研究的目的:
- 引入一个新的端到端自动编码器架构,以实现高效的图像压缩.
- 解决变压器网络在捕获低频图像数据方面的局限性.
主要方法:
- 开发了一个使用双网络架构的变压器和残余网络 (TRN).
- 作为高低频混合器,TRN集成了自我注意路径和残余网络.
- 使用速率扭曲优化 (RDO) 进行端到端的模型训练.
主要成果:
- 与最先进的深度学习方法相比,TRN方法显示出更高的性能.
- 与当前的深度学习方法相比,在CLIC数据集上实现了8.32%的BD率改进.
- 与传统的JPEG方法相比,在CLIC数据集上显示了70.35%的BD率改善.
结论:
- 拟议的TRN有效地保留了高频和低频特征,以实现高效的图像压缩.
- 对于图像压缩,TRN提供了一个全面的解决方案,其性能优于现有的深度学习和传统技术.
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