Multi-conic Gradient Generation via GPU Parallel Processing
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Solution Overview
Problem
Current technologies face challenges in generating complex, visually appealing gradients in real time, requiring complex software and limiting their usability and animation capabilities, as well as restricting real-time rendering to single gradients due to system resource constraints.
Innovation Solution
A method utilizing multiple conics rendered on a computer system with a graphics processing unit (GPU) to compute and animate gradients, allowing for high frame rates and interactive manipulation of gradient parameters, enabling real-time rendering and animation of multiple gradients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex image editing software is used to create gradients, then visual quality is improved, but time consumption and complexity increase
Solution Approach 1:
The patent replaces manual image editing operations with automated gradient generation algorithms. Instead of using complex software tools requiring manual intervention, the system uses mathematical algorithms to automatically generate gradients based on simple parameter inputs, thereby eliminating time-consuming manual operations while maintaining visual quality
Solution Approach 2:
The patent transforms the gradient creation process from complex procedural operations to simple parameter adjustments. By changing the approach from manual tool-based editing to parameter-based algorithmic generation, users can create visually appealing gradients by adjusting a few key parameters, dramatically reducing time consumption while preserving visual quality
2Productivity
If real-time gradient rendering is implemented, then productivity is improved, but system resource requirements increase
Solution Approach 1:
The patent segments the gradient rendering task into multiple independent conic calculations that can be executed in parallel. By dividing the overall gradient computation into separate conic operations, the system can utilize multiple processors or GPU cores simultaneously, achieving real-time rendering performance without overwhelming system resources
Solution Approach 2:
The patent implements a simplified gradient generation approach that computes only the essential conic components needed for visual output. Rather than performing exhaustive calculations, the system calculates the minimum necessary elements to achieve real-time rendering, thereby improving productivity while keeping resource consumption manageable
3Adaptability or versatility
If multiple gradients are processed concurrently, then versatility is improved, but computational complexity increases
Solution Approach 1:
The patent divides multi-gradient processing into independent conic calculation units. Each gradient is processed as a separate set of conic operations, allowing multiple gradients to be handled concurrently through parallel execution of these segmented tasks, thereby improving versatility without significantly increasing overall system complexity
Solution Approach 2:
The patent creates a universal gradient processing framework where the same conic-based algorithm can generate multiple different gradient types. By designing a multi-functional system that handles various gradient styles through a unified mathematical approach, the system achieves high versatility while maintaining relatively simple computational structures that can be efficiently parallelized
Data Source
AI summary
Disclosed herein is a technique for computing a complex gradient using multiple conics. In connection with a computer system having a graphics processing unit (GPU) in addition to the normal central processing unit (CPU), gradients can be computed in real time. The conics may be rendered and adjusted in a number of ways, providing a rich palette for creation of gradient graphics. The computational efficiency of the algorithms disclosed herein, when executed on typical GPU hardware, allows rendering frame rates high enough to provide animated gradient images.