Side Information Driven Image Coding for Machines

With the continuous improvement of computer vision technology, more and more image information is consumed by machines rather than humans. Image coding for machines (ICM) is to compress image data such that they can be more efficiently sent to the receiver side for machines to conduct visual analysi...

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Bibliographic Details
Published in:Picture Coding Symposium pp. 193 - 197
Main Authors: Zhang, Zhongpeng, Liu, Ying
Format: Conference Proceeding
Language:English
Published: IEEE 07.12.2022
Subjects:
Codecs
hyper-prior
image classification
Image coding
image coding for machines
Internet
Receivers
Semantics
side information
Streaming media
transformer
Visualization
ISSN:2472-7822
Online Access:Get full text
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Summary:With the continuous improvement of computer vision technology, more and more image information is consumed by machines rather than humans. Image coding for machines (ICM) is to compress image data such that they can be more efficiently sent to the receiver side for machines to conduct visual analysis. A typical deep learning-based ICM structure contains one codec network which compresses and transmits images through the Internet and one semantic analysis task network such as image classification and object recognition. In the codec part, the side information is the hyper-prior or hierarchical layers of hyper-priors for the compression of image latent representations. In this paper, we propose a Side Information Driven Image Coding (SIIC) framework based on deep learning. It only compresses and transmits the side information to the receiver for image classification tasks. We obtain a top-l accuracy of 70.38% on the ImageNet1K dataset with 0.046 bits per pixel.
ISSN:2472-7822
DOI:10.1109/PCS56426.2022.10018039