Display Engine Interpolation Optimizations for Latency Reduction
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Solution Overview
Problem
Artificial reality systems face challenges in providing immersive experiences due to high computational demands for rendering high-quality graphics and the issue of latency, which can cause virtual reality sickness and discomfort from geometric distortions and slow response to user movements.
Innovation Solution
A system with a primary rendering component and a display engine in a head-mounted display, where the primary rendering component handles complex graphics rendering and the display engine refines and updates the graphics in real-time to reduce latency and correct distortions, using multiple hardware blocks for efficient image processing and output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-quality graphics rendering is performed for artificial reality, then visual quality and immersion are improved, but computational complexity and processing time increase
Solution Approach 1:
The rendering system is divided into two separate components: a primary rendering component that handles computationally intensive tasks (primitive interpretation, shading, texture mapping) and a display engine that handles post-rendering processing (geometric distortion correction, screen-door effect reduction). This segmentation allows each component to be optimized independently, reducing overall computational complexity while maintaining visual quality.
Solution Approach 2:
The primary rendering component performs preliminary rendering operations to generate base images and object primitives before they reach the display engine. By pre-processing the graphics data and preparing it in advance, the system reduces the computational burden on the display engine during real-time operation, thereby lowering overall computational complexity while maintaining high visual quality.
2Loss of time
If real-time graphics updates are performed to respond to user movements, then latency is reduced, but computational load increases
Solution Approach 1:
The display engine performs selective post-rendering processing only on specific aspects of the graphics data that require correction (geometric distortions, screen-door effects) rather than re-processing the entire scene. This partial action approach maintains low latency by avoiding complete re-rendering while still applying necessary corrections in real-time.
Solution Approach 2:
The system extracts only the essential post-rendering processing operations (geometric distortion correction, super-sampling anti-aliasing) from the complete rendering pipeline and handles them separately in the display engine. This extraction allows the system to maintain low latency by focusing computational resources only on the critical corrections needed for immersive experience.
3Object-affected harmful factors
If geometric distortion corrections are applied to reduce virtual reality sickness, then user comfort is improved, but processing time increases
Solution Approach 1:
Geometric distortion correction parameters and lookup tables are pre-computed and stored in memory during system initialization or calibration phases. During real-time operation, the display engine simply applies these pre-computed corrections to the graphics data without performing complex calculations, thereby reducing processing time while still effectively mitigating virtual reality sickness.
Solution Approach 2:
The system applies geometric distortion corrections selectively to specific regions or elements of the display that require correction, rather than uniformly processing the entire image. This partial application approach reduces processing time while still providing sufficient correction to alleviate virtual reality sickness in critical areas.
4Manufacturing precision
If screen-door effect reduction techniques are applied, then visual quality is improved, but computational complexity increases
Solution Approach 1:
The display engine uses super-sampling techniques that involve creating multiple copies or samples of the original image data at different sub-pixel locations, then combining these samples to produce the final output. This copying approach effectively reduces the screen-door effect by filling in gaps between pixels without requiring complex real-time calculations, thereby improving visual quality while maintaining manageable computational complexity.
Data Source
AI summary
In one embodiment, a method includes, by a computing system, accessing a first and second texture associated with an output position, determining a color-blending operation, determining a first color and a first transparency level based on the first texture, determining a second color and a second transparency level based on the second texture, and identifying a color-blending optimization based on the color-blending operation and a comparison of the colors and transparency levels. The method includes determining an output color and an output transparency level by performing the color-blending operation using the colors and transparency levels. The output color is determined by copying the first or second color or the output transparency level is determined by copying the first or second transparency level without additional calculation. The method includes providing the output color and the output transparency level for display at the output position.