Fast lossless images compression for synchrotron radiation facility using deep learning and hybrid architecture  

作　　者：Zhang Min-xing Fu Shi-yuan Gao Yu Cheng Yao-dong Abdulhafiz Ahmed Mustofa Chen Gang 

机构地区：[1]Institute of High Energy Physics,Chinese Academy of Sciences,Beijing,100049,China [2]University of Chinese Academy of Sciences,Beijing,100049,China

出　　处：《Radiation Detection Technology and Methods》2024年第4期1693-1703,共11页辐射探测技术与方法(英文)

摘　　要：Purpose The rapid growth in image data generated by high-energy photon sources poses significant challenges for storage and analysis,with conventional compression methods offering compression ratios often below 1.5.Methods This study introduces a novel,fast lossless compression method that combines deep learning with a hybrid computing architecture to overcome existing compression limitations.By employing a spatiotemporal learning network for predictive pixel value estimation and a residual quantization algorithm for efficient encoding.Results When benchmarked against the DeepZip algorithm,our approach demonstrates a 40%reduction in compression time while maintaining comparable compression ratios using identical computational resources.The implementation of a GPU+CPU+FPGA hybrid architecture further accelerates compression,reducing time by an additional 38%.Conclusions This study presents an innovative solution for efficiently storing and managing large-scale image data from synchrotron radiation facilities,harnessing the power of deep learning and advanced computing architectures.

关 键 词：Lossless compression Deep learning Heterogeneous architecture Synchrotron radiation image 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]