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End-to-End Optimized Lossy Compression for Neural-Morphic Spiking Camera Captured Data

文献类型：期刊论文

作者Feng, Kexiang1,7,8; Jia, Chuanmin5; Pan, Jingshan6; Ma, Siwei3,4; Gao, Wen2,3,4
刊名IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
出版日期2025-06-01
卷号35期号:6页码:6074-6086
关键词Image coding Cameras Image reconstruction Streams Correlation Redundancy Visualization Firing Spatial resolution Retina Spike compression spatio-temporal contextual compression attention mechanism end-to-end spike coding
ISSN号1051-8215
DOI10.1109/TCSVT.2025.3530947
英文摘要Recently, the bio-inspired spike camera with continuous motion recording capability has attracted tremendous attention due to its ultra high temporal resolution imaging characteristic. Such imaging feature results in huge data storage and transmission burden compared to that of traditional camera, raising severe challenge and imminent necessity in compression for spike camera captured content. Existing lossy data compression methods could not be applied for compressing spike streams efficiently due to integrate-and-fire characteristic and binarized data structure. Considering the imaging principle and information fidelity of spike cameras, we propose a novel Reconstruction-based Contextual Spike Compression (RCSC) framework, which contains scene reconstruction, contextual image compression and spike generation. To our knowledge, it is the first learning-based model for efficient and robust spike stream compression with informative fidelity. Extensive experimental results show that our model outperforms the state-of-the-art conventional codec VVC intra by 6.14% and surpasses the state-of-the-art learned codec by 2.53% in BD-rate reduction, establishing a strong baseline for spike compression.
资助项目National Natural Science Foundation of China[62025101] ; Beijing Natural Science Foundation[4252003] ; New Cornerstone Science Foundation through the XPLORER PRIZE ; Peking University High-Performance Computing Platform
WOS研究方向Engineering
语种英语
WOS记录号WOS:001506717400039
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/42363]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Jia, Chuanmin; Ma, Siwei
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Beijing 100190, Peoples R China
3.Peng Cheng Lab, Shenzhen 518055, Peoples R China
4.Peking Univ, Natl Engn Res Ctr Visual Technol, Sch Comp Sci, Beijing 100871, Peoples R China
5.Peking Univ, Wangxuan Inst Comp Technol, Beijing 100080, Peoples R China
6.Shandong Comp Sci Ctr Nat Supercomp Jinan, Jinan 250014, Peoples R China
7.Peking Univ, Natl Engn Res Ctr Visual Technol, Beijing 100871, Peoples R China
8.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Feng, Kexiang,Jia, Chuanmin,Pan, Jingshan,et al. End-to-End Optimized Lossy Compression for Neural-Morphic Spiking Camera Captured Data[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2025,35(6):6074-6086.
APA Feng, Kexiang,Jia, Chuanmin,Pan, Jingshan,Ma, Siwei,&Gao, Wen.(2025).End-to-End Optimized Lossy Compression for Neural-Morphic Spiking Camera Captured Data.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,35(6),6074-6086.
MLA Feng, Kexiang,et al."End-to-End Optimized Lossy Compression for Neural-Morphic Spiking Camera Captured Data".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 35.6(2025):6074-6086.

入库方式： OAI收割

来源：计算技术研究所

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