Color Sampling Method for Noise Reduction in Touch-Controlled Image Sensors
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Solution Overview
Problem
Camera applications on mobile devices introduce significant noise in image sensors, especially under low-light or high ISO conditions, leading to incorrect or unreal color representation in digital photos, making pixel sampling complex and difficult.
Innovation Solution
A color sampling method that utilizes temporal and spatial analysis to reduce noise by determining representative color values through averaging or median calculations within a first region and applying weights based on distance to a touch input, followed by spatial analysis to determine candidate color values within a smaller second region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel sampling is performed under low-light or high ISO conditions, then color information can be obtained, but significant noise is introduced causing wrong or unreal colors
Solution Approach 1:
The sampling region is divided into a first region for temporal analysis and a second region for spatial analysis. This segmentation allows the patent to process pixels in different stages: first reducing noise through temporal averaging across multiple frames, then refining color accuracy through spatial weighted averaging within the second region. This two-stage segmentation effectively separates noise reduction from color sampling, solving the contradiction between obtaining color information and avoiding noise.
Solution Approach 2:
The patent performs temporal analysis on the first region before performing spatial analysis on the second region. By preliminarily processing the image data through temporal averaging to reduce noise, and then applying spatial weighted averaging, the system prepares the data in advance to ensure accurate color sampling without being affected by noise. This preliminary action of noise reduction before final color determination resolves the contradiction.
2Ease of manufacture
If traditional pixel sampling is performed without noise reduction, then sampling process is simple, but significant noise causes wrong color representation
Solution Approach 1:
The sampling process is segmented into two distinct regions with different processing methods. The first region undergoes temporal analysis across multiple image frames to reduce noise, while the second region performs spatial weighted averaging. This segmentation transforms a potentially complex single-step process into two manageable stages, maintaining ease of implementation while significantly improving color accuracy through systematic noise reduction.
Solution Approach 2:
The first region serves as an intermediary processing stage between the raw image data and the final color sampling in the second region. By introducing this intermediate temporal analysis step, the patent mediates the conflict between simplicity and accuracy: the first region handles noise reduction through straightforward temporal averaging, while the second region focuses on precise color determination through spatial analysis, making the overall process both simple and accurate.
3Measurement precision
If noise reduction techniques are applied to improve color accuracy, then color representation becomes accurate, but the sampling process becomes more complex
Solution Approach 1:
The patent divides the sampling process into two clearly defined regions with specific functions. The first region handles temporal analysis for noise reduction, while the second region performs spatial weighted averaging for color determination. This segmentation organizes the complexity into modular, manageable steps, making the sophisticated noise reduction process easier to implement and understand compared to a single complex algorithm.
Solution Approach 2:
Different processing qualities are applied to different regions: the first region receives temporal analysis with equal weighting across multiple frames for noise reduction, while the second region receives spatial weighted averaging with distance-based weights for precise color sampling. This local quality approach ensures that each region gets the appropriate processing complexity needed for its specific function, optimizing the balance between accuracy and complexity.
Data Source
AI summary
A color sampling method includes detecting a touch input on a touch screen, determining a first region corresponding to the touch input, performing a temporal analysis on the first region of a plurality of image frames to determine representative color values of pixels within the first region, determining a second region within the first region, and performing a spatial analysis on the second region to determine candidate color value corresponding to the touch input according to the representative color values of pixels within the second region.