Foveated Image Generation with Resolution Functions
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Solution Overview
Problem
Rendering high-resolution images is computationally expensive, particularly due to the need to process and display images with varying levels of detail across different parts of the image, as humans have weak peripheral vision, leading to inefficiencies in processing power and bandwidth usage.
Innovation Solution
Implementing foveation techniques, where the image resolution varies based on the user's gaze, with higher resolution at the fovea and decreasing in an inverse linear fashion towards the periphery, using resolution functions that characterize the resolution in the display space as a function of angle, allowing for the generation of foveated images that require less processing power and data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution images are rendered across the entire display area, then image quality is improved, but computational cost and processing time increase significantly
Solution Approach 1:
The patent applies different resolution levels to different regions of the display based on human visual sensitivity. The foveal region (center of gaze) receives high-resolution rendering while peripheral regions receive lower resolution, matching the biological特性 of human vision where the fovea has high acuity and peripheral vision has lower acuity. This resolves the contradiction by maintaining high image quality where needed while reducing computational burden in less critical areas.
Solution Approach 2:
The display area is segmented into multiple resolution zones including a foveal high-resolution region and peripheral low-resolution regions. This segmentation allows the system to allocate computational resources selectively to different segments, improving overall processing efficiency while maintaining perceived image quality through the resolution constraint function that defines boundaries between resolution levels.
2Measurement precision
If uniform high resolution is applied across the entire image, then image quality is improved, but data transmission requirements and bandwidth usage increase
Solution Approach 1:
Different data densities are applied to different spatial regions, with high data volume concentrated in the foveal region and reduced data volume in peripheral regions. This local quality approach ensures that image quality is maintained where human vision is most sensitive while significantly reducing overall data transmission requirements through the resolution constraint function.
3Productivity
If resolution varies across different portions of the image, then computational efficiency is improved, but image quality may deteriorate in peripheral areas
Solution Approach 1:
The resolution constraint function dynamically adjusts the resolution parameter across different spatial locations and gaze positions. By changing the resolution parameter based on the user's foveal position and peripheral regions, the system maintains high perceived image quality while improving processing efficiency. The function ensures smooth transitions between resolution levels to avoid visible artifacts.
Data Source
AI summary
In one implementation, a method of generating an image is performed by a device including one or more processors and non-transitory memory. The method includes generating a first resolution function based on a formula with a set of variables having a first set of values. The method includes generating a first image based on first content and the first resolution function. The method includes detecting a resolution constraint. The method includes generating a second resolution function based on the formula with the set of variables having a second set of values, wherein the second resolution function has a summation value that satisfies the resolution constraint. The method includes generating a second image based on second content and the second resolution function.