Color-Pattern Convolution Filtering for Faster Mobile Image Processing
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Solution Overview
Problem
Deep learning technologies face challenges in mobile devices due to high computational requirements, leading to prolonged processing times and reduced image quality.
Innovation Solution
An image processing device selects convolution filters based on color pattern information of input image data to perform convolution operations, reducing computational complexity and enhancing image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning technology is applied to mobile devices, then image processing capability is improved, but processing time increases and computational power requirements increase
Solution Approach 1:
The patent applies different convolution filters to different unit regions of the input image data based on their color pattern information. Instead of using a single uniform filter for the entire image, the system selects appropriate filters (e.g., first convolution filter for first unit region, second convolution filter for second unit region) that are optimized for specific color characteristics, thereby improving processing efficiency and reducing computation time while maintaining image quality
Solution Approach 2:
The input image data is divided into multiple unit regions, and each region is processed independently with selected convolution filters. This segmentation approach allows the system to reduce computational complexity by processing smaller regions with appropriate filters rather than applying complex deep learning models to the entire image, thus reducing processing time while maintaining capability
2Productivity
If network complexity is reduced to decrease processing time, then processing speed is improved, but image quality deteriorates
Solution Approach 1:
The system maintains image quality by selecting convolution filters that are specifically optimized for different color patterns in different unit regions. Each region receives the most appropriate filter for its color characteristics, ensuring that image quality is preserved while the overall network complexity is reduced through selective filtering rather than uniform application of complex models
Solution Approach 2:
The patent changes the parameters of the convolution operation by selecting different weight values for different convolution filters based on color pattern information. This allows the system to adapt the processing parameters to local image characteristics, maintaining image quality while reducing computational requirements through parameter optimization rather than increasing network complexity
3Loss of time
If convolution filters with different weight values are selected based on color pattern information, then computation time is reduced, but filter selection complexity increases
Solution Approach 1:
The system performs preliminary analysis of the input image data to extract color pattern information before applying convolution operations. By pre-processing the image data to identify color patterns and determine appropriate filter selections in advance, the system reduces computation time during the actual convolution process while managing filter selection complexity through structured pre-analysis
Data Source
AI summary
An image processing method performed by an image processing device may include receiving input image data from an image sensor, selecting a convolution filter corresponding to each unit region from among a plurality of convolution filters based on color pattern information of the input image data, and generating, based on the selected convolution filter, a first image to be displayed from the input image data.