Diffusion-based Aesthetic QR Code Generation via Scanning-Robust Perceptual Guidance

• URL: http://arxiv.org/abs/2403.15878v1
• Date: Sat, 23 Mar 2024 16:08:48 GMT
• Title: Diffusion-based Aesthetic QR Code Generation via Scanning-Robust Perceptual Guidance
• Authors: Jia-Wei Liao, Winston Wang, Tzu-Sian Wang, Li-Xuan Peng, Cheng-Fu Chou, Jun-Cheng Chen,
• Abstract summary: QR codes, prevalent in daily applications, lack visual appeal due to their conventional black-and-white design. We introduce a novel diffusion-model-based aesthetic QR code generation pipeline, utilizing pre-trained ControlNet and guided iterative refinement. With extensive quantitative, qualitative, and subjective experiments, the results demonstrate that the proposed approach can generate diverse aesthetic QR codes with flexibility in detail.
• Score: 9.905296922309157
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: QR codes, prevalent in daily applications, lack visual appeal due to their conventional black-and-white design. Integrating aesthetics while maintaining scannability poses a challenge. In this paper, we introduce a novel diffusion-model-based aesthetic QR code generation pipeline, utilizing pre-trained ControlNet and guided iterative refinement via a novel classifier guidance (SRG) based on the proposed Scanning-Robust Loss (SRL) tailored with QR code mechanisms, which ensures both aesthetics and scannability. To further improve the scannability while preserving aesthetics, we propose a two-stage pipeline with Scanning-Robust Perceptual Guidance (SRPG). Moreover, we can further enhance the scannability of the generated QR code by post-processing it through the proposed Scanning-Robust Projected Gradient Descent (SRPGD) post-processing technique based on SRL with proven convergence. With extensive quantitative, qualitative, and subjective experiments, the results demonstrate that the proposed approach can generate diverse aesthetic QR codes with flexibility in detail. In addition, our pipelines outperforming existing models in terms of Scanning Success Rate (SSR) 86.67% (+40%) with comparable aesthetic scores. The pipeline combined with SRPGD further achieves 96.67% (+50%). Our code will be available https://github.com/jwliao1209/DiffQRCode.

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