Brush Attribute Determination for Image Rendering Process Generation
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Solution Overview
Problem
The existing methods for generating an image rendering process are inefficient due to high performance consumption in determining the attributes of multiple brushes, leading to low efficiency and time-consuming processes.
Innovation Solution
A method that determines the attributes of brush objects on a first brush layer based on detail parameter values of sampled pixels, stores these attributes in a brush queue, and generates a rendering process by sequencing the brush objects, thereby avoiding the complexity of solving an optimal solution to a mathematical model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the attributes of multiple brushes are determined by establishing and solving a mathematical model for each brush, then the accuracy of brush attribute determination is improved, but the computational complexity and performance consumption increase significantly
Solution Approach 1:
The patent segments the image into multiple brush layers, where each layer corresponds to a specific brush type. This segmentation allows independent processing of each brush layer without requiring complex global optimization across all brushes, thereby reducing computational complexity while maintaining attribute determination accuracy for each segment.
Solution Approach 2:
The patent performs preliminary actions by pre-defining brush templates and their associated mathematical models before actual image processing. These pre-established models are stored and reused during rendering, eliminating the need to solve complex optimization problems in real-time and significantly reducing computational burden.
2Manufacturing precision
If the attributes of each brush are determined through solving a mathematical model, then the quality of the rendering process is improved, but the time consumption increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining brush templates and their associated mathematical models before actual image processing. These pre-established models are stored and reused during rendering, eliminating the need to solve complex optimization problems in real-time and significantly reducing computational burden.
Solution Approach 2:
The patent uses brush templates as reusable copies that can be applied multiple times across different regions of the image. Instead of solving a new mathematical model for each brush instance, the system copies and adapts pre-solved templates, maintaining rendering quality while dramatically reducing time consumption.
3Quantity of substance
If a large quantity of brushes are involved in drawing the image, then the detail and complexity of the rendering is improved, but the performance consumption and processing time increase
Solution Approach 1:
The patent segments the large quantity of brushes into multiple organized layers, where each layer contains brushes of a specific type or function. This segmentation enables parallel processing of different layers and efficient management of large numbers of brushes, maintaining detail and complexity while improving overall processing efficiency.
Solution Approach 2:
The patent creates universal brush templates that can serve multiple functions across different layers and regions. A single template can be instantiated and applied repeatedly with different parameters, allowing the system to handle large quantities of brushes efficiently through reuse rather than creating unique models for each brush instance.
Data Source
AI summary
An image drawing process generation method and apparatus, a device, and a storage medium. The method comprises: determining the brush size and brush position information of a brush object on a first brush layer on the basis of a detail parameter value of a pixel sampling point on the first brush layer set for a target image (S101); then, saving the brush object on the first brush layer into a brush queue corresponding to the first brush layer (S102); and finally, generating, on the basis of an attribute of each brush object in the brush queue, a drawing process of a target style image corresponding to the target image (S103). The brush size and brush position information of a corresponding brush object are determined on the basis of a detail parameter value of each pixel sampling point, and determinations of attributes of brush objects are independent from each other, which improves the determination efficiency of the attributes of the brush objects, thereby improving the generation efficiency of an image drawing process.