Procedural Image Engine Cache Management for Memory Optimization
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Solution Overview
Problem
Current graphics applications face challenges in efficiently handling and manipulating large amounts of data for textures, leading to high memory consumption, slow data transfer, and repetitive content, with procedural textures being difficult to create and manipulate effectively.
Innovation Solution
An image generation engine that uses sequence graphs to optimize the generation and rendering of images by reusing intermediate results and recalculating unsaved ones, allowing for real-time image generation and efficient storage of descriptions rather than final images, enabling the generation of infinite variations from basic textures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If procedural textures are used to avoid repetitions and reduce storage, then storage efficiency and adaptability improve, but ease of creation and manipulation deteriorates
Solution Approach 1:
The patent introduces an intermediary tool that sits between the procedural generation system and the graphic designer. This tool translates designer intent into procedural parameters automatically, serving as a mediator that bridges the gap between manual creation ease and procedural efficiency. The intermediary handles the complexity of procedural parameter adjustment while presenting a simple interface to designers.
Solution Approach 2:
The system enables self-service by allowing procedural textures to automatically adapt and generate variations based on predefined parameters and algorithms. The procedural system serves itself by automatically adjusting parameters, generating variations, and optimizing storage without requiring manual intervention for each texture variation, thus improving both storage efficiency and ease of use.
2Ease of operation
If textures are stored as bitmaps with fixed resolution, then ease of storage and retrieval improves, but adaptability to different contexts deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from static bitmap storage to dynamic procedural generation. Textures are no longer fixed-resolution bitmaps but dynamic procedures that can adapt their resolution and appearance based on runtime requirements. The system dynamically adjusts texture parameters, resolution, and generation based on the specific context and performance needs.
Solution Approach 2:
The system utilizes parameter changes to enable adaptability. Instead of storing fixed-resolution bitmaps, the patent stores and manipulates procedural parameters that can be dynamically adjusted. By changing parameters such as resolution, scale, and generation algorithms, the same procedural texture can adapt to different contexts while maintaining ease of storage through compact parameter representation.
3Productivity
If intermediate results are stored in memory for reuse, then calculation speed improves, but memory consumption increases
Solution Approach 1:
The patent applies local quality by selectively caching intermediate results based on their specific characteristics and reuse potential. Not all intermediate results are stored equally - the system identifies and caches only those results that are likely to be reused, storing them with appropriate quality levels. This selective approach optimizes both calculation speed and memory consumption by focusing resources on the most beneficial caches.
Solution Approach 2:
The system implements discarding and recovering by managing the cache of intermediate results. When memory consumption becomes excessive, less frequently used intermediate results are discarded from memory. When needed again, these results are recalculated (recovered) rather than maintaining them permanently in memory. This cyclic approach balances calculation speed improvements against memory consumption constraints.
Data Source
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AI summary
The invention relates to an image-generating engine capable of generating images for a host application from procedural data, including: a module for scanning a sequential procedural graph for image generation; a module for calculating intermediate results; a module for calculating the time for rendering intermediate results; a module for calculating the weight of the intermediate results; memory modules for storing the graph G and the corresponding lists L0, L1, L2, L3 and M; a comparison and deletion module, suitable for comparing the available memory space for storing new intermediate result data or previously existing data with a given minimum threshold, and for deleting intermediate result data if said memory space available for storing new intermediate result data is lower than said threshold.