Easz: An Agile Transformer-based Image Compression Framework for Resource-constrained IoTs

Neural image compression, necessary in various machine-to-machine communication scenarios, suffers from its heavy encode-decode structures and inflexibility in switching between different compression levels. Consequently, it raises significant challenges in applying the neural image compression to e...

Full description

Bibliographic Details

Published in:	2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors:	Mao, Yu, Li, Jingzong, Wang, Jun, Xu, Hong, Kuo, Tei-Wei, Guan, Nan, Xue, Chun Jason
Format:	Conference Proceeding
Language:	English
Published:	IEEE 22.06.2025
Subjects:	Computational efficiency Erase-and-Squeeze Image coding Image Compression Image edge detection Image reconstruction Machine-to-machine communications Performance evaluation Receivers Servers Switches Transformer-based Auto-Encoder Transformers
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!