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...
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| Vydáno v: | 2025 62nd ACM/IEEE Design Automation Conference (DAC) s. 1 - 7 |
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IEEE
22.06.2025
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| Abstract | 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 edge devices that are developed for powerful servers with high computational and storage capacities. We take a step to solve the challenges by proposing a new transformer-based edge-computefree image coding framework called Easz. Easz shifts the computational overhead to the server, and hence avoids the heavy encoding and model switching overhead on the edge. Easz utilizes a patch-erase algorithm to selectively remove image contents using a conditional uniform-based sampler. The erased pixels are reconstructed on the receiver side through a transformer-based framework. To further reduce the computational overhead on the receiver, we then introduce a lightweight transformer-based reconstruction structure to reduce the reconstruction load on the receiver side. Extensive evaluations conducted on a realworld testbed demonstrate multiple advantages of Easz over existing compression approaches, in terms of adaptability to different compression levels, computational efficiency, and image reconstruction quality. |
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| AbstractList | 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 edge devices that are developed for powerful servers with high computational and storage capacities. We take a step to solve the challenges by proposing a new transformer-based edge-computefree image coding framework called Easz. Easz shifts the computational overhead to the server, and hence avoids the heavy encoding and model switching overhead on the edge. Easz utilizes a patch-erase algorithm to selectively remove image contents using a conditional uniform-based sampler. The erased pixels are reconstructed on the receiver side through a transformer-based framework. To further reduce the computational overhead on the receiver, we then introduce a lightweight transformer-based reconstruction structure to reduce the reconstruction load on the receiver side. Extensive evaluations conducted on a realworld testbed demonstrate multiple advantages of Easz over existing compression approaches, in terms of adaptability to different compression levels, computational efficiency, and image reconstruction quality. |
| Author | Li, Jingzong Xue, Chun Jason Kuo, Tei-Wei Xu, Hong Wang, Jun Mao, Yu Guan, Nan |
| Author_xml | – sequence: 1 givenname: Yu surname: Mao fullname: Mao, Yu email: yu.mao@mbzuai.ac.ae organization: Mohamed bin Zayed University of Artificial Intelligence,UAE – sequence: 2 givenname: Jingzong surname: Li fullname: Li, Jingzong email: jingzongli@hsu.edu.hk organization: The Hang Seng University of Hong Kong,Hong Kong – sequence: 3 givenname: Jun surname: Wang fullname: Wang, Jun email: jwang699-c@my.cityu.edu.hk organization: City University of Hong Kong,Hong Kong – sequence: 4 givenname: Hong surname: Xu fullname: Xu, Hong email: hongxu@cuhk.edu.hk organization: The Chinese University of Hong Kong,Hong Kong – sequence: 5 givenname: Tei-Wei surname: Kuo fullname: Kuo, Tei-Wei email: ktw@csie.ntu.edu.tw organization: National Taiwan University,Taiwan – sequence: 6 givenname: Nan surname: Guan fullname: Guan, Nan email: nanguan@my.cityu.edu.hk organization: City University of Hong Kong,Hong Kong – sequence: 7 givenname: Chun Jason surname: Xue fullname: Xue, Chun Jason email: jason.xue@mbzuai.ac.ae organization: Mohamed bin Zayed University of Artificial Intelligence,UAE |
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| Snippet | Neural image compression, necessary in various machine-to-machine communication scenarios, suffers from its heavy encode-decode structures and inflexibility in... |
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| SubjectTerms | 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 |
| Title | Easz: An Agile Transformer-based Image Compression Framework for Resource-constrained IoTs |
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