Pixel Color Prediction Models for Rendering Efficiency
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Solution Overview
Problem
Current computer graphics technologies face challenges in achieving high spatial and temporal resolution due to repeated processing in rasterization pipelines, leading to performance constraints and aliasing issues, especially with limited samples per pixel.
Innovation Solution
A system that assigns predictive models to individual pixels to predict pixel colors over time, comparing predicted colors to input information and reassigning models based on similarity, allowing for reduced geometry processing and shading efforts while maintaining color accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If rasterization pipelines recompute each frame image from scratch, then spatial and temporal resolution can be maintained, but computational resources are excessively consumed
Solution Approach 1:
The patent performs preliminary actions by computing high-resolution images at lower frame rates and storing them as historical data. This pre-computed information is then reused in subsequent frames through predictive models, avoiding the need to recompute all geometry and shading from scratch while maintaining spatial and temporal resolution.
Solution Approach 2:
The patent creates copies of previously computed pixel data and uses predictive models to generate approximations of current frame pixels based on historical data. These copied and predicted pixel values replace expensive real-time computations, significantly reducing the computational load while preserving image quality.
2Manufacturing precision
If supersampling is used to reduce aliasing, then rendering quality improves, but memory requirements and computational costs increase
Solution Approach 1:
Instead of storing multiple supersampled versions of images in memory, the patent copies and stores pixel data over time in exponential history buffers. Predictive models then use these temporal copies to infer current pixel values, achieving aliasing reduction through time-based sampling rather than spatial supersampling, thereby reducing memory requirements.
Solution Approach 2:
The patent changes the sampling parameter from spatial dimension (supersampling) to temporal dimension (historical buffer depth). By adjusting the number of historical frames stored and the predictive model parameters, the system achieves equivalent or better anti-aliasing performance with reduced memory usage compared to traditional supersampling methods.
3Measurement precision
If more samples per pixel are used, then rendering accuracy improves, but processing time and computational load increase
Solution Approach 1:
The patent copies pixel data across multiple time frames and uses predictive models to combine these temporal samples. This approach achieves high rendering accuracy by leveraging historical information without requiring multiple samples per pixel in the current frame, thereby reducing processing time while maintaining or improving accuracy.
Solution Approach 2:
The patent maintains continuous useful action by reusing previously computed pixel data across frames through predictive models. Instead of discarding past computations, the system continuously incorporates historical pixel information into current frame rendering, reducing redundant calculations and processing time while maintaining accuracy.
Data Source
AI summary
Views of a virtual space may be presented based on predicted colors of individual pixels of individual frame images that depict the views of the virtual space. Predictive models may be assigned to individual pixels that predict individual pixel colors of individual pixels at individual time points. Individual models may be updated and/or reprojected to other pixels based on comparisons of the predicted pixel colors and colors specified by in a raster input signal.