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Efficient decoder for learned image compression via structured pruning

Citation Author(s):
Liewen Liao, Shaohui Li, Jixiang Luo, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
Submitted by:
liewen liao
Last updated:
28 February 2022 - 11:09am
Document Type:
Poster
Document Year:
2022
Event:
Presenters:
Liewen Liao
Paper Code:
124
 

We propose a structured pruning method to achieve a light-weighted decoder of learned image compression to accommodate various terminals. The structured pruning method identifies the effectiveness of each channel of decoder via gradient ascent and gradient descent while maintaining the encoder and entropy model. To our best knowledge, this paper is the first attempt to design a structured pruning method for universal pretrained learned image compression. Experimental results demonstrate that the proposed method can reduce about 40% parameters and save 25% inference time at the cost of 0.23 dB BD-PSNR and 4.33\% BD-rate change.

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