摘要
在图像大数据应用背景下,伴随着硬件技术的高速发展,基于深度学习的图像视频编码技术逐渐成熟。基于端到端学习的压缩框架因能更高效地对原始图像数据进行紧致表达,在学术界和工业界都得到了广泛的关注。系统地总结了基于端到端学习的图像压缩框架中的核心模块如变换、量化、熵编码和损失函数的研究现状,对其研究进展和关键技术进行了概括性的介绍,并对前沿研究成果进行了性能比较。
In the big data era,we have witnessed the explosive growth of deep learning based image and video compression technologies.Such end-to-end learning-based compression frameworks have demonstrated promising efficiency for compact representation of original image data,and attracted a vast attention from both academia and industry.A systematic review of transformation,quantization,entropy coding,and loss function used in end-to-end learning-based image compression framework is introduced in this work.The research progress and key technologies are briefly introduced,as well as the comparative studies of coding performance for existing methods with leading efficiency.
作者
陈积敏
林泽昊
Chen Jimin;Lin Zehao(Nanjing Forest Police College,Nanjing,Jiangsu 210023,China;College of Information Science and Technology,Donghua University,Shanghai 201620,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2020年第22期20-30,共11页
Laser & Optoelectronics Progress
基金
国家林业局软科学项目(2017-R06)
江苏省哲学社会科学优秀科技创新团队(生态环境保护执法)。
关键词
图像处理
图像压缩
端到端学习
深度学习
image processing
image compression
end-to-end learning
deep learning
作者简介
林泽昊,E-mail:lzhtocoffee@163.com。
