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TERA: Screen-to-Camera Image Code With Transparency, Efficiency, Robustness and Adaptability

delete2022-01-01
delete35
PRE
AI
H
Han Fang
D
Dongdong Chen
王锋 (Feng Wang)
Z
Zehua Ma
H
Honggu Liu
W
Wenbo Zhou
张卫明 (Weiming Zhang)
N
Nenghai Yu *
DOI:10.1109/TMM.2021.3061801delete
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摘要

摘要

En 中文
With the rapid development of digital devices, the issue of how to transmit information among different devices with multimedia carriers has drawn much attention from the research community. This paper focuses on the important user scenario of screen-to-camera information transmission. Along this direction, image coding-based techniques have been shown to be the most popular and effective methods in the past decades. However, after careful study, we find that none of the existing methods can satisfy the four important properties simultaneously, i.e., high transparency, high embedding efficiency, strong transmission robustness and high adaptability to device types. This is mainly because these properties are contradictory with each other. In this paper, we thus propose a screen-to-camera image code dubbed TERA (transparency, efficiency, robustness and adaptability), which makes it possible to circumvent the contradiction among the above four properties for the first time. Generally, TERA adopts the color decomposition principle to ensure the visual quality and the superposition-based scheme to ensure embedding efficiency. BCH-coding-based information arrangement and a powerful attention-guided information decoding network are further designed to guarantee the robustness and adaptability. Through extensive experiments, the superiority and broad applications of our method are demonstrated.
Keyword:
Robustness
Cameras
Distortion
Visualization
Image coding
Decoding
Two dimensional displays
Adaptability
attention-guided
color decomposition
efficiency
robustness
screen-to-camera image code
transparency

期刊

IEEE Transactions on Multimedia 封面图
IEEE Transactions on Multimedia
IF:
9.7
论文数:
4.5K
被引数:
2.4W

机构

U
university of science & technology of china, cas
学者数:
3.2W
论文数: 2.7W
被引数: 74
C
chinese academy of sciences
学者数:
56.7W
论文数: 45.0W
被引数: 704
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