Adaptive GPU Anti-Aliasing Filtering by Pixel Density Threshold
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Solution Overview
Problem
Multi-camera automotive vision systems face challenges in generating output images with minimal aliasing effects due to different transformations and sub/sampling processes, which introduce visual artefacts like aliasing and blurring, especially in highly detailed content, and existing GPU architectures struggle to implement simple filtering operations efficiently for real-time performance.
Innovation Solution
The method involves calculating pixel density values for each output pixel and selectively applying anti-aliasing filtering based on these values, using bilinear interpolation and weighted averaging of neighboring pixel brightness values, reducing the number of pixel read operations by performing four-pixel reads only when pixel density exceeds a threshold, thereby minimizing processing time and load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If anti-aliasing filtering is applied to all pixels in the output image, then aliasing artefacts are reduced, but processing time and computational load increase significantly
Solution Approach 1:
The patent applies anti-aliasing filtering selectively based on local pixel density characteristics. Instead of uniformly filtering all pixels, the system calculates pixel density for each output pixel and applies filtering only to pixels exceeding a density threshold. This local differentiation resolves the contradiction by concentrating computational resources on regions where aliasing is most likely to occur (high-density areas) while skipping low-density regions, thereby reducing overall processing time while maintaining aliasing suppression effectiveness.
2Productivity
If simple filtering operations are implemented on GPU architecture, then real-time performance is achieved, but filtering effectiveness is reduced compared to DSP architectures
Solution Approach 1:
The patent segments the filtering operation into two distinct paths: a fast path for low-density pixels that requires minimal computation, and a full filtering path for high-density pixels that achieves comprehensive aliasing suppression. This segmentation allows the GPU to process the majority of pixels efficiently while dedicating full filtering resources only where necessary, thus maintaining real-time performance while preserving filtering effectiveness in critical regions.
Solution Approach 2:
The patent implements partial filtering action by applying anti-aliasing filtering to only a subset of pixels (those exceeding the density threshold) rather than all pixels. This partial action is sufficient to eliminate visible aliasing artefacts while significantly reducing the computational burden on the GPU, thereby achieving the desired balance between real-time performance and filtering effectiveness.
3Productivity
If pixel density calculation and selective filtering is implemented, then processing load is reduced by 66%, but device complexity increases
Solution Approach 1:
The patent performs preliminary pixel density calculation for all output pixels before the filtering stage. This preliminary action creates a density map that guides subsequent filtering operations, allowing the system to identify and skip low-density pixels early in the pipeline. By performing this classification upfront, the system avoids unnecessary filtering computations and achieves significant processing load reduction, while the added complexity is confined to a single preliminary pass that can be efficiently implemented on GPU hardware.
Data Source
AI summary
A method for reducing abasing artefacts in an output image may include obtaining a plurality of input images captured by a plurality of cameras, each camera having a different field of view of an environment surrounding a vehicle, wherein the plurality of input images are mapped to the output image to represent the environment, from a predefined virtual point of view. The method may further include for each pixel position in the output image, obtaining a first pixel density value corresponding to a first output pixel position in the output image; and upon determining that the first pixel density value is higher than a threshold, calculating a first output brightness value corresponding to the first output pixel position based at least on a plurality of brightness values corresponding to a plurality of neighboring pixels of a corresponding position in a first input image of the plurality of input images.