Texture Data Handling with Precomputed MIP Filters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for rendering computer-generated images are resource-intensive due to serializing techniques in reading and calculating filters for texture tiles, leading to extended render times or constraints on the number of texture tiles and filters calculated.
Innovation Solution
A rendering system that applies precomputed filters to MIP levels, using fitted two-dimensional filters and caching to reduce computation and caching requirements, allowing for parallel processing and efficient texture processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If serializing techniques are used for reading tiles and calculating filters, then resource usage is controlled, but render time is extended and productivity is reduced
Solution Approach 1:
The patent pre-calculates filter functions and stores them in a cache before rendering begins. During rendering, these pre-computed filters are retrieved and applied to texture tiles, eliminating the need to calculate filters during the rendering process itself. This preliminary action significantly reduces render time while maintaining quality.
Solution Approach 2:
The patent separates the filter calculation process from the tile reading and rendering process. Filter calculation is performed independently in advance, while rendering focuses on retrieving pre-computed filters and applying them to tiles. This segmentation allows parallel processing and eliminates serialization bottlenecks.
2Manufacturing precision
If the number of texture tiles and filters calculated is increased, then image quality is improved, but resource intensity increases
Solution Approach 1:
Filter functions are pre-calculated and cached before rendering, allowing the system to use high-quality filters without increasing real-time computational resources. The expensive filter calculations are performed in advance when resources are available, not during rendering when resources are constrained.
Solution Approach 2:
The patent creates copies of pre-computed filter functions and stores them in a cache for repeated use during rendering. Instead of recalculating the same filters multiple times, the system retrieves cached copies, significantly reducing computational resource intensity while maintaining the ability to apply high-quality filters to many tiles.
3Adaptability or versatility
If filters are calculated during rendering, then adaptability to different tiles is maintained, but processing time increases
Solution Approach 1:
The patent pre-calculates filter functions that are designed to be universally applicable to different texture tiles. These adaptive filters are computed in advance and cached, allowing the system to maintain adaptability to different tiles while avoiding the time cost of recalculating filters during rendering.
Solution Approach 2:
The patent creates filter functions that can be applied universally to multiple different texture tiles. Instead of calculating unique filters for each tile, the system develops versatile filter functions that work across different tile types and scenarios, reducing the total number of calculations needed while maintaining adaptability.
Data Source
AI summary
An imagery processing system that combines MIP level filtering with spatial filtering when rendering images. Filtering can be performed in an order that optimizes memory accesses during the rendering process.