Saliency Map-Guided End-to-End Image Coding for Machines

Existing end-to-end image coding for machines (ICM) methods generally use joint training strategies to promote the compression efficiency for machine vision without considering the influence of different regions in the image. To encourage the image compression network to focus on the regions that ar...

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Vydané v:IEEE signal processing letters Ročník 31; s. 1755 - 1759
Hlavní autori: Peng, Bo, Lin, Tianxiang, Jin, Dengchao, Pan, Zhaoqing, Lei, Jianjun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
end-to-end image compression
Error detection
Image coding
Image coding for machines
Image compression
Machine vision
mean square error loss
Object detection
Object recognition
Salience
saliency map
Semantics
Task analysis
Training
Visual tasks
Visualization
ISSN:1070-9908, 1558-2361
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Popis
Shrnutí:Existing end-to-end image coding for machines (ICM) methods generally use joint training strategies to promote the compression efficiency for machine vision without considering the influence of different regions in the image. To encourage the image compression network to focus on the regions that are critical to the subsequent visual task, this paper proposes a saliency map-guided image compression network (SMIC-Net) for ICM. Specifically, a saliency map-guided transform module (SMTM) is proposed to improve the representation ability of image features for object detection task by exploring the semantic and structural information of the detected object. Besides, a saliency map-guided mean square error (SM-MSE) loss is designed to place more emphasis on the detected object regions. Experimental results demonstrate that the proposed SMIC-Net effectively promotes the compression efficiency for machine vision.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2024.3420178