Hybrid Dithering Processor Selection for Image Rendering
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Solution Overview
Problem
Current methods for dithering in print rendering systems are inefficient as they often rely solely on either CPU or GPU processing, failing to maximize performance due to data dependency and inefficient selection between processors based on pixel position, leading to suboptimal processing of tiles with different spatial properties.
Innovation Solution
The Hybrid Dithering (HD) method selects the appropriate CPU or GPU processor for dithering based on the size and attribute types of regions within a tile, merging adjacent regions with the same attributes and comparing their size to a threshold to determine the most efficient processor for dithering, thereby optimizing performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dithering is performed using a single processor (CPU or GPU), then the system is simple to implement, but processing performance is not maximized due to data dependency and inefficient task distribution
Solution Approach 1:
The patent divides the dithering processing task into segments based on pixel position and attribute types. Different regions of the image are assigned to different processors (CPU or GPU) depending on their characteristics, allowing parallel processing while managing complexity through structured task segmentation
Solution Approach 2:
The patent implements dynamic processor selection where the system adaptively chooses between CPU and GPU based on real-time analysis of pixel attributes and regional characteristics. This dynamic approach optimizes performance by matching task characteristics with appropriate processor capabilities rather than using a static assignment
2Speed
If dithering tasks are distributed across multiple processors, then processing speed improves, but the system complexity increases due to need for task management and coordination
Solution Approach 1:
The patent applies local quality by analyzing specific regional characteristics (attribute types, pixel positions) and assigning tasks to processors based on local needs rather than uniform distribution. This allows optimized processing for different image regions while simplifying overall task management through localized decision-making rules
3Productivity
If all pixels are processed using the same method, then the processing approach is simple, but performance is suboptimal for tiles with different spatial properties
Solution Approach 1:
The patent changes processing parameters based on spatial properties of different tile regions. By analyzing attribute types and pixel positions, the system adjusts which processor handles each region, optimizing efficiency for diverse spatial characteristics while maintaining a unified processing framework
Data Source
AI summary
A method of dithering pixels of a graphical representation is described, the method comprising the steps of receiving the graphical representation which comprises a plurality of regions each being characterized by an attribute type associated with content of the region; selecting, from a group of processors comprising at least a central processing unit (CPU) and a graphics processing unit (GPU), at least one processor to dither pixels of the regions depending upon corresponding sizes and attribute types of the regions; and dithering pixels of the graphical representation using the selected processor.