Deep learning image compression with multi-channel tANS coding and hardware deployment

Deep learning-based image compression outperforms traditional methods in coding efficiency, but its computational complexity hinders real-time deployment on embedded devices. This paper proposes a heterogeneous computing system combining GPU-accelerated inference and CPU-accelerated entropy coding v...

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Vydáno v:	Journal of real-time image processing Ročník 23; číslo 1; s. 1
Hlavní autoři:	Zhu, Yaohua, Zhang, Yong, Liu, Ya, Jiang, Jingyu, Zhu, Yanghang, Huang, Mingsheng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2026 Springer Nature B.V
Témata:	Algorithms Co-design Coding Compression ratio Computer Graphics Computer Science Deep learning Efficiency Embedded systems Entropy Graphics processing units Hardware Image compression Image Processing and Computer Vision Image quality Lookup tables Multimedia Information Systems Neural networks Pattern Recognition Real time Signal,Image and Speech Processing Wavelet transforms Entropy coding Deep learning image compression tANS coding Cache-friendly GPU
ISSN:	1861-8200, 1861-8219
On-line přístup:	Získat plný text
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