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Quantization error in regular grids: triangular pixels.
Summary
Image pixel quantization causes feature location errors. This study quantifies these errors for triangular pixels, finding their Mean Absolute Error (MAE) is comparable to square pixels.
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Area of Science:
- Computer Vision
- Image Processing
- Computational Geometry
Background:
- Image quantization into pixels inherently loses feature location precision.
- This loss introduces errors in quantities derived from feature positions.
- Previous work analyzed errors for square and hexagonal pixel tessellations.
Discussion:
- This paper derives analytic expressions for error distribution, Mean Absolute Error (MAE), and Mean Square Error (MSE) for triangular tessellation.
- The analysis uses a linear approximation for differentiable functions of independently quantized points.
- These error metrics are crucial for assessing the sensitivity of image processing algorithms.
Key Insights:
- Closed-form expressions for error metrics (MAE, MSE) due to triangular pixel quantization are derived.
- The Mean Absolute Error (MAE) for triangular tessellation (D(T)) is found to be within 0.99 to 1.13 times that of square tessellation (D(S)).
- This indicates comparable error magnitudes between triangular and square pixel arrangements.
Outlook:
- Further research could explore other pixel shapes or non-linear function approximations.
- The derived analytic expressions can be integrated into image processing algorithm design.
- Understanding quantization error is vital for developing more accurate image analysis techniques.

