Sub-pixel Intensity Weighting for Display Distortion Compensation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing techniques for compensating optical distortions in display systems, such as HUDs, often require multiple passes or complex interpolation methods, leading to latency and inefficiencies in achieving high-quality image rendering.
Innovation Solution
A single-pass method for determining the intensity of a sub-pixel position within a pixelated image by calculating a weighted average of adjacent pixel intensities, which minimizes latency and preserves image quality by compensating for distortions in a single processing step.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple passes or complex interpolation methods are used for image distortion compensation, then image quality is improved, but processing latency increases
Solution Approach 1:
The patent segments the image into pixel blocks and processes each block independently using a lookup table. This segmentation allows the system to avoid complex global interpolation while maintaining local image quality, resolving the contradiction between image quality and processing latency.
Solution Approach 2:
The patent pre-calculates distortion compensation data and stores it in lookup tables before actual image processing. This preliminary action eliminates the need for complex real-time calculations during image rendering, significantly reducing processing latency while maintaining image quality through accurate pre-computed compensation values.
2Measurement precision
If multiple passes are used for distortion compensation, then rendering accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent performs distortion compensation calculations in advance and stores results in lookup tables. This preliminary computation phase separates the complex calculations from the real-time rendering phase, reducing processing complexity during actual image display while maintaining high rendering accuracy through pre-computed data.
Solution Approach 2:
The patent uses lookup tables that store pre-computed distortion compensation data as copies of the actual computation results. This copying approach replaces complex real-time calculations with simple table lookups, reducing processing complexity while preserving rendering accuracy by using pre-calculated values.
3Manufacturing precision
If complex interpolation methods are used, then image quality is improved, but computational overhead increases
Solution Approach 1:
The patent divides the image into pixel blocks and applies simple interpolation within each block using pre-stored lookup table data. This segmentation reduces the computational scope of each operation, lowering computational overhead while maintaining image quality through localized accurate compensation.
Solution Approach 2:
The patent uses pre-computed interpolation results stored in lookup tables instead of performing complex interpolation calculations in real-time. This copying of computation results eliminates redundant calculations, significantly reducing computational overhead and energy consumption while preserving image quality.
Data Source
AI summary
A method for determining intensity of a sub-pixel position within a pixelated image to enhance the image quality thereof. In a first step of this method information is received regarding a first pixel at a position X0,Y0, having an intensity I(X0,Y0). In a second step information is received regarding a second pixel at a position X1,Y0, where said second pixel is adjacent to and to the right of said first pixel. In third step, information is received regarding a third pixel at a position X0,Y1, where the third pixel is adjacent to and below the first pixel. In a fourth step information is received regarding a fourth pixel at a position X1,Y1, where the fourth pixel is adjacent to and to the right of the third pixel. A pixel value, I(X,Y), is then calculated where I(X,Y)=(((Nx−(Nx*(X−X0)))*(Ny−(Ny*(Y−Y0)))*I(X0,Y0))+((Nx−(Nx*(X1−X)))*(Ny−(Ny*(Y−Y0)))*I(X1,Y0))+((Nx−(Nx*(X−X0)))*(Ny−(Ny*(Y1−Y)))*I(X0,Y1))+((Nx−(Nx*(X−X)))*(Ny−(Ny*(Y1−Y)))*I(X1,Y1)))/(Ny*Nx), wherein the digital granularity is Nx per lateral pixel, Ny per vertical pixel and wherein X,Y is a sub-pixel position provided from an upper level system.