Non-Planar Display Sampling for Distortion Correction
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Solution Overview
Problem
Current projection technologies face challenges in displaying images on non-planar surfaces without geometric and photometric distortion, leading to aliasing artifacts due to undersampling and oversampling in nonlinear image mapping (NLIM) processes.
Innovation Solution
The system analyzes distortion parameters to determine optimal image resolution and configures filters for resampling, minimizing aliasing while controlling computational complexity by calculating maximum inflation and deflation, and using these parameters to adjust the filter settings in the trans-raster distortion correction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If nonlinear image mapping is used to correct distortion on non-planar surfaces, then geometric accuracy is improved, but aliasing artifacts increase due to undersampling and oversampling
Solution Approach 1:
The patent pre-calculates the distortion correction parameters and sampling patterns before rendering. By determining the optimal sampling distribution in advance based on the predicted distortion field, the system prepares correction data that compensates for upcoming undersampling/oversampling regions, reducing aliasing artifacts before they occur during actual image mapping
Solution Approach 2:
The patent applies different sampling densities to different regions of the image based on local distortion characteristics. Areas with high distortion gradients receive higher sampling density to prevent aliasing, while low-distortion regions use lower density to maintain performance. This local adaptation of sampling quality resolves the contradiction between geometric accuracy and aliasing reduction
2Object-affected harmful factors
If high-resolution linear-projected image is used to prevent undersampling, then aliasing is reduced, but computational complexity increases
Solution Approach 1:
The patent dynamically adjusts the resolution parameter of the linear-projected image based on the calculated distortion characteristics. By changing the resolution parameter adaptively - using higher resolution only where and when distortion requires it - the system reduces aliasing without consistently maintaining high computational complexity across the entire image
Solution Approach 2:
The patent applies high-resolution rendering only to specific regions or frames where distortion-induced aliasing is most likely to occur, rather than uniformly across the entire image sequence. This partial application of high resolution reduces overall computational complexity while still addressing aliasing problems where they manifest most severely
3Manufacturing precision
If distortion correction filtering is applied to minimize aliasing, then image quality is improved, but processing time increases
Solution Approach 1:
The patent pre-computes filtering parameters and kernel configurations based on anticipated distortion patterns before the actual image mapping operation. By preparing filter settings in advance, the system reduces the real-time processing burden during distortion correction, maintaining image quality while minimizing processing time loss
Solution Approach 2:
The patent employs dynamic filtering that adapts its strength and characteristics based on local distortion gradients and motion. Rather than applying uniform heavy filtering everywhere, the system dynamically adjusts filter intensity - applying stronger filtering only where distortion causes aliasing - thus improving image quality selectively without proportionally increasing processing time across the entire image
Data Source
AI summary
Sampling in the process of trans-raster distortion correction is described. The distortion parameterization is analyzed to determine the maximum inflation and deflation (magnification and minification) over the image. The maximum inflation is then used to determine the optimal resolution (dimensions in pixels) of the linear-projected image such that it is not undersampled by the output image. The maximum deflation, coupled with the optimal resolution determined in the inflation step, is then used to configure the filter used in the resampling process such that aliasing due to undersampling is minimized, while simultaneously controlling the computational burden of the filter.