VisCode: Embedding Information in Visualization Images using Encoder-Decoder Network
This addresses the problem of data embedding in visualizations for users in information visualization, though it appears incremental as it builds on existing encoder-decoder and QR code methods.
The authors tackled the problem of embedding information into visualization images without distortion by developing VisCode, a deep encoder-decoder network that uses visualization images and QR codes as training data, achieving effective perceptual quality and decoding success rates in evaluations.
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.