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Text2QR: Harmonizing Aesthetic Customization and Scanning Robustness for Text-Guided QR Code Generation

Guangyang Wu, Xiaohong Liu, Jun Jia, Xuehao Cui, Guangtao Zhai

TL;DR

The QR Aesthetic Blueprint module is introduced, generating a blueprint image exerting control over the entire generation process, and the Scannability Enhancing Latent Refinement (SELR) process refines the output iteratively in the latent space, enhancing scanning robustness.

Abstract

In the digital era, QR codes serve as a linchpin connecting virtual and physical realms. Their pervasive integration across various applications highlights the demand for aesthetically pleasing codes without compromised scannability. However, prevailing methods grapple with the intrinsic challenge of balancing customization and scannability. Notably, stable-diffusion models have ushered in an epoch of high-quality, customizable content generation. This paper introduces Text2QR, a pioneering approach leveraging these advancements to address a fundamental challenge: concurrently achieving user-defined aesthetics and scanning robustness. To ensure stable generation of aesthetic QR codes, we introduce the QR Aesthetic Blueprint (QAB) module, generating a blueprint image exerting control over the entire generation process. Subsequently, the Scannability Enhancing Latent Refinement (SELR) process refines the output iteratively in the latent space, enhancing scanning robustness. This approach harnesses the potent generation capabilities of stable-diffusion models, navigating the trade-off between image aesthetics and QR code scannability. Our experiments demonstrate the seamless fusion of visual appeal with the practical utility of aesthetic QR codes, markedly outperforming prior methods. Codes are available at \url{https://github.com/mulns/Text2QR}

Text2QR: Harmonizing Aesthetic Customization and Scanning Robustness for Text-Guided QR Code Generation

TL;DR

The QR Aesthetic Blueprint module is introduced, generating a blueprint image exerting control over the entire generation process, and the Scannability Enhancing Latent Refinement (SELR) process refines the output iteratively in the latent space, enhancing scanning robustness.

Abstract

In the digital era, QR codes serve as a linchpin connecting virtual and physical realms. Their pervasive integration across various applications highlights the demand for aesthetically pleasing codes without compromised scannability. However, prevailing methods grapple with the intrinsic challenge of balancing customization and scannability. Notably, stable-diffusion models have ushered in an epoch of high-quality, customizable content generation. This paper introduces Text2QR, a pioneering approach leveraging these advancements to address a fundamental challenge: concurrently achieving user-defined aesthetics and scanning robustness. To ensure stable generation of aesthetic QR codes, we introduce the QR Aesthetic Blueprint (QAB) module, generating a blueprint image exerting control over the entire generation process. Subsequently, the Scannability Enhancing Latent Refinement (SELR) process refines the output iteratively in the latent space, enhancing scanning robustness. This approach harnesses the potent generation capabilities of stable-diffusion models, navigating the trade-off between image aesthetics and QR code scannability. Our experiments demonstrate the seamless fusion of visual appeal with the practical utility of aesthetic QR codes, markedly outperforming prior methods. Codes are available at \url{https://github.com/mulns/Text2QR}
Paper Structure (24 sections, 14 equations, 9 figures, 2 tables)

This paper contains 24 sections, 14 equations, 9 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Aesthetic 2D Barcode.
  4. Diffusion Based Generative Models.
  5. Preliminary
  6. Method
  7. Overall
  8. QR Aesthetic Blueprint
  9. Histogram polarization.
  10. Module reorganization.
  11. Adaptive-Halftone blending.
  12. Scannability Enhancing Latent Refinement
  13. Marker loss.
  14. Harmonizing loss.
  15. Experiments
  16. ...and 9 more sections

Figures (9)

  • Figure 1: Aesthetic QR codes (first row) and their zoom-in counterparts (second row) generated by Text2QR. Our QR codes not only exhibit exceptional scanning robustness but also showcase allure and intricate details, accommodating a diverse range of customized styles.
  • Figure 2: Illustration of preserving scanning-robustness. Each module in our QR code (e.g. $I_{M_i}$ and $I_{M_j}$) is correspondingly mapped to white (green arrow) or black (yellow arrow) blocks, collectively forming a standard QR code target We use blue masks to filter the white and black modules for better visualization.
  • Figure 3: Overall Structure of the Text2QR. The pipeline consists of three stages, denoted with orange, blue and black lines. We propose the QAB module for generating a blueprint image used as controlling guidance, and propose the SELR module for refining the controlled output to enhance its scanning robustness.
  • Figure 4: Visualization of the process of Histogram Polarization.
  • Figure 5: Comparison of blueprint images and their corresponding ControlNet output. Utilizing a pure QR code as the blueprint (first column) yields a low error level $e$ but lacks semantic features. Employing a fixed size of $u=\frac{a}{3}$ (second column) leads to a substantial error level. Our Adaptive-Halftone blending method preserves realistic image content with a minimal error level.
  • ...and 4 more figures