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Performance of QR Code Detectors near Nyquist Limits.

Przemysław Skurowski1, Karolina Nurzyńska2, Magdalena Pawlyta1

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This study simulates QR code scanning near Shannon

Keywords:
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Area of Science:

  • Computer Vision
  • Augmented Reality
  • Image Processing

Background:

  • Augmented reality (AR) requires efficient object recognition.
  • 2D barcode tags, like QR codes, aid AR by identifying physical objects.
  • Higher camera resolutions enable smaller QR code detection but increase computational load.

Purpose of the Study:

  • To simulate QR code scanning near the limits imposed by Shannon's theorem.
  • To analyze the performance of decoders under varying image capture conditions.
  • To evaluate the trade-offs between processing time and recognition accuracy.

Main Methods:

  • Simulated QR code scanning near Shannon's theorem limits.
  • Analyzed three public decoders (Zbar, OpenCV, and another) against size-to-sampling ratios and Modulation Transfer Function (MTF).
  • Modeled MTF using Gaussian low-pass filtering based on real device characteristics.

Main Results:

  • A minimum QR code module size of 3-3.5 pixels is recommended for practical decoding, irrespective of MTF.
  • Zbar demonstrated superior performance in practical decoding tasks.
  • OpenCV showed the weakest recognition but excelled in decoding information at the Nyquist limit and below.

Conclusions:

  • Practical QR code recognition requires modules of at least 3-3.5 pixels.
  • Decoder choice impacts AR system performance, with Zbar being generally better and OpenCV useful for borderline cases.
  • Understanding MTF and resolution limits is crucial for optimizing AR object recognition.