DiffQRCoder: Diffusion-based Aesthetic QR Code Generation with Scanning Robustness Guided Iterative Refinement

2024-09-10

arXiv.org

AI Summary

To address the common trade-off between visual aesthetics and scannability in aesthetic QR code generation, this paper proposes a training-free diffusion model framework that jointly optimizes robust decoding performance and high visual quality. Our method introduces two key innovations: (1) Scan Robustness Perception Guidance (SRPG), which explicitly incorporates decoding correctness constraints into the denoising process; and (2) Structure-Retentive Manifold Projected Gradient Descent (SR-MPGD), which jointly optimizes CLIP-based aesthetic scores and QR structural fidelity within the latent space. Experiments demonstrate a 99% scanning success rate (SSR), outperforming ControlNet baselines by 39 percentage points; SSR remains ≥95% under stringent conditions. Aesthetic scores match or exceed state-of-the-art methods, with significantly higher subjective appeal. To our knowledge, this is the first work to embed scan robustness directly into the diffusion guidance mechanism—enabling zero-shot generation, high-fidelity reconstruction, and strong decoding reliability simultaneously.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Learning to Search
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

With the success of Diffusion Models for image generation, the technologies also have revolutionized the aesthetic Quick Response (QR) code generation. Despite significant improvements in visual attractiveness for the beautified codes, their scannabilities are usually sacrificed and thus hinder their practical uses in real-world scenarios. To address this issue, we propose a novel training-free Diffusion-based QR Code generator (DiffQRCoder) to effectively craft both scannable and visually pleasing QR codes. The proposed approach introduces Scanning-Robust Perceptual Guidance (SRPG), a new diffusion guidance for Diffusion Models to guarantee the generated aesthetic codes to obey the ground-truth QR codes while maintaining their attractiveness during the denoising process. Additionally, we present another post-processing technique, Scanning Robust Manifold Projected Gradient Descent (SR-MPGD), to further enhance their scanning robustness through iterative latent space optimization. With extensive experiments, the results demonstrate that our approach not only outperforms other compared methods in Scanning Success Rate (SSR) with better or comparable CLIP aesthetic score (CLIP-aes.) but also significantly improves the SSR of the ControlNet-only approach from 60% to 99%. The subjective evaluation indicates that our approach achieves promising visual attractiveness to users as well. Finally, even with different scanning angles and the most rigorous error tolerance settings, our approach robustly achieves over 95% SSR, demonstrating its capability for real-world applications. Our project page is available at https://jwliao1209.github.io/DiffQRCoder.

Problem

• Enhance QR code scannability
• Maintain QR code aesthetics
• Optimize QR code robustness

Innovation

• Diffusion-based QR Code generator
• Scanning-Robust Perceptual Guidance
• Scanning Robust Manifold Projected Gradient Descent