VisCode: Embedding Information in Visualization Images using Encoder-Decoder Network

We present an approach called VisCode for embedding information into visualization images. This technology can implicitly embed data information specified by the user into a visualization while ensuring that the encoded visualization image is not distorted. The VisCode framework is based on a deep n...

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Veröffentlicht in:	IEEE transactions on visualization and computer graphics Jg. 27; H. 2; S. 326 - 336
Hauptverfasser:	Zhang, Peiying, Li, Chenhui, Wang, Changbo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.02.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Artificial neural networks autocoding Coders Data visualization Decoding Embedding Encoders-Decoders Encoding Image coding Image color analysis information steganography Information visualization Media Performance evaluation saliency detection Scientific visualization Visualization visualization retargeting
ISSN:	1077-2626, 1941-0506, 1941-0506
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Abstract	We present an approach called VisCode for embedding information into visualization images. This technology can implicitly embed data information specified by the user into a visualization while ensuring that the encoded visualization image is not distorted. The VisCode framework is based on a deep neural network. We propose to use visualization images and QR codes data as training data and design a robust deep encoder-decoder network. The designed model considers the salient features of visualization images to reduce the explicit visual loss caused by encoding. To further support large-scale encoding and decoding, we consider the characteristics of information visualization and propose a saliency-based QR code layout algorithm. We present a variety of practical applications of VisCode in the context of information visualization and conduct a comprehensive evaluation of the perceptual quality of encoding, decoding success rate, anti-attack capability, time performance, etc. The evaluation results demonstrate the effectiveness of VisCode.
AbstractList	We present an approach called VisCode for embedding information into visualization images. This technology can implicitly embed data information specified by the user into a visualization while ensuring that the encoded visualization image is not distorted. The VisCode framework is based on a deep neural network. We propose to use visualization images and QR codes data as training data and design a robust deep encoder-decoder network. The designed model considers the salient features of visualization images to reduce the explicit visual loss caused by encoding. To further support large-scale encoding and decoding, we consider the characteristics of information visualization and propose a saliency-based QR code layout algorithm. We present a variety of practical applications of VisCode in the context of information visualization and conduct a comprehensive evaluation of the perceptual quality of encoding, decoding success rate, anti-attack capability, time performance, etc. The evaluation results demonstrate the effectiveness of VisCode.We present an approach called VisCode for embedding information into visualization images. This technology can implicitly embed data information specified by the user into a visualization while ensuring that the encoded visualization image is not distorted. The VisCode framework is based on a deep neural network. We propose to use visualization images and QR codes data as training data and design a robust deep encoder-decoder network. The designed model considers the salient features of visualization images to reduce the explicit visual loss caused by encoding. To further support large-scale encoding and decoding, we consider the characteristics of information visualization and propose a saliency-based QR code layout algorithm. We present a variety of practical applications of VisCode in the context of information visualization and conduct a comprehensive evaluation of the perceptual quality of encoding, decoding success rate, anti-attack capability, time performance, etc. The evaluation results demonstrate the effectiveness of VisCode. We present an approach called VisCode for embedding information into visualization images. This technology can implicitly embed data information specified by the user into a visualization while ensuring that the encoded visualization image is not distorted. The VisCode framework is based on a deep neural network. We propose to use visualization images and QR codes data as training data and design a robust deep encoder-decoder network. The designed model considers the salient features of visualization images to reduce the explicit visual loss caused by encoding. To further support large-scale encoding and decoding, we consider the characteristics of information visualization and propose a saliency-based QR code layout algorithm. We present a variety of practical applications of VisCode in the context of information visualization and conduct a comprehensive evaluation of the perceptual quality of encoding, decoding success rate, anti-attack capability, time performance, etc. The evaluation results demonstrate the effectiveness of VisCode.
Author	Zhang, Peiying Li, Chenhui Wang, Changbo
Author_xml	– sequence: 1 givenname: Peiying surname: Zhang fullname: Zhang, Peiying organization: School of Computer Science and TechnologyEast China Normal University – sequence: 2 givenname: Chenhui surname: Li fullname: Li, Chenhui email: chli@cs.ecnu.edu.cn organization: School of Computer Science and TechnologyEast China Normal University – sequence: 3 givenname: Changbo surname: Wang fullname: Wang, Changbo organization: School of Computer Science and TechnologyEast China Normal University
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SubjectTerms	Algorithms Artificial neural networks autocoding Coders Data visualization Decoding Embedding Encoders-Decoders Encoding Image coding Image color analysis information steganography Information visualization Media Performance evaluation saliency detection Scientific visualization Visualization visualization retargeting
Title	VisCode: Embedding Information in Visualization Images using Encoder-Decoder Network
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