摘要
图像是人们获取信息的重要途径之一。随着图像传输与存储需求的不断增加,尤其在带宽受限或云存储情况下,对图像进行极低比特率压缩,对于提高传输效率和节省存储空间具有重要意义。基于此,本文对有损图像极低比特率压缩技术进行了系统综述。首先,在基于生成对抗网络(Generative adversarial networks,GAN)的图像压缩衍生算法在高分辨率图像压缩、生成图像模糊、忽视语义信息与纹理信息等方面问题的基础上,介绍了最新的极低比特率图像压缩方法。然后,阐述了分层压缩、基于对象和感兴趣区域等其他非GAN模型的极低比特率图像压缩方法。接着,描述了常用数据集及有损压缩条件下的图像质量评价方法。最后,对极低比特率有损图像压缩技术做出总结,并对其后续的发展进行了展望。
Image is one of the important ways to obtain information.With the increasing demand of image transmission and storage,especially in bandwidth limited or cloud storage situations,compressing images at extremely low bitrates is of great significance for improving transmission efficiency and saving storage space.Based on this,this paper presents a systematic review of very low bitrate compression techniques for lossy images.Firstly,on the basis of problems of image compression derivative algorithms based on generative adversarial network(GAN)in terms of high-resolution image compression,generating image blur,and neglecting semantic and texture information,the latest very low bitrate image compression methods are introduced.Then,this paper elaborates image compression methods that achieve very low bitrate using non-GAN models such as layered compression,object based,and region of interest.After that,the commonly used datasets and image quality evaluation methods under lossy compression conditions are described.Finally,a summary of very low bitrate lossy image compression techniques are made,and an outlook on their subsequent development is given.
作者
岳爽
陈喆
殷福亮
YUE Shuang;CHEN Zhe;YIN Fuiang(School of Information and Communication Engineering,Dalian University of Technology,Dalian 116024,China)
出处
《数据采集与处理》
北大核心
2025年第1期102-116,共15页
Journal of Data Acquisition and Processing
基金
国家自然科学基金(61871066,62271103)。
关键词
图像压缩
极低比特率
图像有损编码
深度学习
生成对抗网络
image compression
very low bitrate
image lossy encoding
deep learning
generative adversarial network(GAN)
