Geometric Distortion Correction via Tile-Based Sub-Window Processing
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Solution Overview
Problem
Existing methods for correcting geometric distortion in images captured with wide-angle lenses, such as fisheye lenses, are limited in flexibility and often result in visible artefacts, especially when trying to display wide-angle views without distortion.
Innovation Solution
A method and system that process image data by receiving transformation data to adjust geometric distortion, dividing the input frame into sub-windows, and applying different transformations to each sub-window, allowing for flexible correction of distortion and reduced artefacts in wide-angle views.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a single transformation is applied to the entire input frame, then the processing is simple and fast, but visible artefacts appear and distortion correction is insufficient
Solution Approach 1:
The input frame is divided into multiple overlapping tiles, and each tile is further divided into sub-tiles. This segmentation allows different transformation parameters to be applied to different regions, improving distortion correction accuracy while managing processing complexity through hierarchical organization.
Solution Approach 2:
Different transformation parameters are applied to different tiles and sub-tiles based on their local characteristics. Each region receives customized transformation treatment, enabling precise distortion correction for wide-angle views while maintaining overall image quality.
2Productivity
If the entire input frame is processed as one unit, then processing is straightforward, but processing efficiency decreases and artefacts increase
Solution Approach 1:
The frame is segmented into multiple tiles that can be processed independently and in parallel. This increases processing efficiency while reducing artefacts through localized transformation application. The overlapping regions between tiles ensure continuity and reduce visible boundaries.
Solution Approach 2:
Processing is applied selectively to different portions of the frame with appropriate transformation strength. Overlapping processing regions provide excessive coverage that eliminates artefacts at tile boundaries while maintaining overall efficiency.
3Adaptability or versatility
If different transformations are applied to different sub-windows, then distortion correction flexibility improves, but processing complexity increases
Solution Approach 1:
The frame is divided into tiles and sub-tiles that can receive different transformation parameters. This segmentation enables flexible adaptation to local distortion characteristics while managing complexity through hierarchical organization and reusable processing blocks.
Solution Approach 2:
The same processing pipeline and transformation algorithms are reused across all tiles and sub-tiles, providing universal functionality. This multi-use approach enables flexible transformation application while reducing overall system complexity through code reuse and standardized processing blocks.
Data Source
AI summary
A method including receiving input data representing an input frame of a video. Transformation data representing at least one transformation for applying to an image to adjust a geometric distortion of the image is received. A first and second plurality of input tiles, each including a respective portion of the input frame, is processed with at least part of the transformation data, thereby generating first and second sub-window data representing a first and second sub-window of an output frame of the video, respectively.