The results of VisCode
VisCode can be used in a variety of practical applications. The input is a static visualization image. According to the encoding rules, we can retarget the original visual style after performing the decoding operation. Visualization retargeting includes representation (a) and theme color (b). VisCode can be also applied to metadata embedding as shown in (c) and large-scale information embedding. After decoding the source code into a static image, we can quickly build a visualization application and apply a common interaction such as selection as shown in (d).


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.


[Paper Arxiv], to appear in IEEE VIS 2020

  title={VisCode: Embedding Information in Visualization Images using Encoder-Decoder Network
  author = {Peiying Zhang and Chenhui Li and Changbo Wang},
  booktitle={IEEE Transactions on Visualization and Computer Graphics},


Chenhui Li