Image Signal Processor False-Color Evaluation Using Reference Images
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Solution Overview
Problem
Existing methods for evaluating the performance of image signal processors lack accuracy and consistency, leading to variations in image quality due to subjective judgments and suboptimal settings.
Innovation Solution
A method for evaluating image signal processor performance by selecting target colors, identifying false color pixels, generating a reference image by changing their colors, and calculating color errors to quantify performance, thereby enabling objective and consistent evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing evaluation methods are used, then evaluation process is simple, but measurement precision and consistency deteriorate due to subjective judgments
Solution Approach 1:
The evaluation method segments the image into multiple color channels (e.g., red, green, blue) and evaluates false color pixels in each channel separately. This segmentation allows for precise measurement of color accuracy by analyzing each color component independently, thereby improving measurement precision while maintaining a systematic evaluation framework.
Solution Approach 2:
The patent introduces an intermediary reference image generated by changing false color pixels to their correct colors. This reference image serves as a mediator between the original image and the evaluation metrics, enabling objective comparison and quantification of color accuracy without direct subjective judgment.
2Reliability
If subjective judgment methods are used, then evaluation process is simple, but reliability deteriorates due to variations in image quality assessment
Solution Approach 1:
The evaluation system incorporates feedback mechanisms by calculating color errors between corresponding pixels in the original image and the reference image. This quantitative feedback provides consistent and reliable evaluation results that can be objectively compared across different images and processing conditions, eliminating variability introduced by subjective judgment.
Solution Approach 2:
The patent changes the evaluation parameter from subjective quality assessment to objective color error measurement. By quantifying color accuracy through numerical parameters (color differences between pixels), the system achieves reliable and consistent evaluation results that are independent of human judgment variations.
3Manufacturing precision
If traditional evaluation methods are used, then processing time is short, but manufacturing precision deteriorates in sensor sorting and yield optimization
Solution Approach 1:
The method performs preliminary action by generating a reference image that identifies false color pixels before the actual evaluation measurement. This preparatory step organizes the evaluation data in advance, allowing for efficient subsequent processing and reducing the time required for detailed analysis during sensor sorting operations.
Solution Approach 2:
The patent replaces manual or mechanical evaluation processes with automated computational methods. By using algorithms to automatically detect false color pixels, generate reference images, and calculate color errors, the system achieves high manufacturing precision for sensor sorting while maintaining efficient processing speeds through automation.
Data Source
AI summary
A method for evaluating performance of an image signal processor according to various example embodiments includes: selecting at least two target colors from an original image generated by the image signal processor; defining a true color range in each of the target colors and identifying at least one false color pixel having a color deviating from the true color range in the original image; generating a reference image by changing a color of the false color pixel; and calculating a color error between the original image and the reference image.