Hardware Background Removal Using RGB to YCrCb Conversion
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Solution Overview
Problem
Existing image processing systems in multifunction devices struggle to efficiently remove background noise and colors in RGB color space, requiring significant hardware logic and lacking efficient methods to quantify pixel colorfulness for effective background removal.
Innovation Solution
The system converts RGB pixels to YCrCb color space using bit-shift operations, evaluates pixel characteristics by comparing color space components to threshold values, and implements background removal using minimal hardware logic, reducing the number of logic gates and enhancing processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional background removal methods are implemented in hardware, then processing speed is improved, but hardware logic complexity increases
Solution Approach 1:
The patent extracts only the essential background removal functionality from complex software algorithms and implements it in hardware. By taking out the core histogram-based background identification and pixel value thresholding operations, the system achieves hardware-speed processing while minimizing unnecessary logic complexity.
Solution Approach 2:
The patent uses simple, resource-efficient hardware components that can be easily manufactured and replaced. The background removal logic is implemented using basic logic gates and lookup tables rather than complex programmable structures, creating a simple, fast, and cost-effective hardware solution.
2Ease of manufacture
If background removal logic is minimized in ASIC design, then manufacturing cost is reduced, but processing capability may be limited
Solution Approach 1:
The patent changes the operational parameters of the background removal algorithm to suit hardware implementation. Instead of using complex multi-dimensional histograms, the system uses simplified 1D histograms based on luminance values and straightforward thresholding, maintaining effectiveness while reducing logic requirements.
Solution Approach 2:
The background removal process is segmented into distinct hardware stages: luminance calculation, histogram construction, threshold determination, and pixel classification. This segmentation allows each stage to be implemented with simple, dedicated logic, reducing overall complexity while maintaining processing capability.
3Measurement precision
If color space conversion is implemented accurately, then color accuracy is improved, but hardware resource usage increases
Solution Approach 1:
The patent uses simplified, approximate color space conversion formulas that can be implemented with basic arithmetic operations. Rather than using full-precision matrix multiplication, the system employs scaled integer arithmetic and bit-shifting operations that maintain sufficient accuracy for background removal while using minimal hardware resources.
Solution Approach 2:
The patent applies color space conversion only where necessary for background removal decisions, rather than converting entire images with full precision. The conversion is performed locally on a per-pixel basis using simplified formulas, providing sufficient color information for background identification without the overhead of complete color accuracy.
Data Source
AI summary
Pixel characteristics, such as whether a pixel forms part of the background of an image, are evaluated by computing histograms from pixel color space components, by comparing pixel color space components to threshold values, or by other methods.