Image Sensor SNR Evaluation Using Circular Test Chart
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Solution Overview
Problem
Existing methods for evaluating and optimizing image sensor performance are inadequate, particularly in accurately determining signal-to-noise ratio (SNR) dips and adjusting settings to improve image quality, especially in high dynamic range (HDR) mode.
Innovation Solution
A method involving the use of a test chart with an evaluation area having multiple unit areas of varying brightness, disposed centrally, to generate test images that allow for precise calculation of SNR and adjustment of dual conversion gain settings to prevent SNR dips, thereby optimizing image sensor performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to evaluate image sensor performance, then the evaluation process is simple, but the measurement precision of signal-to-noise ratio and performance evaluation accuracy are insufficient
Solution Approach 1:
The test chart is divided into multiple unit areas (first through nth unit areas) with different brightness levels arranged in a circular pattern. Each unit area corresponds to different signal-to-noise ratio conditions, enabling precise measurement of SNR characteristics across various brightness levels. This segmentation allows the evaluation system to measure performance at multiple discrete points rather than using a single uniform test area.
Solution Approach 2:
Different regions of the test chart (different unit areas) have different brightness characteristics tailored to specific measurement needs. The circular arrangement with varying brightness levels allows local optimization of measurement conditions for different signal-to-noise ratio ranges, enabling precise evaluation of image sensor performance under diverse lighting conditions.
2Reliability
If dual conversion gain settings are adjusted to prevent SNR dips, then image quality in HDR mode is improved, but the device complexity and setting optimization difficulty increase
Solution Approach 1:
The evaluation method measures the actual signal-to-noise ratio at different brightness levels and unit areas, providing feedback information about where SNR dips occur. This feedback enables optimization of dual conversion gain settings by identifying specific brightness ranges and spatial regions where performance degradation occurs, allowing targeted adjustment of conversion gain parameters to eliminate SNR dips in HDR mode.
Solution Approach 2:
The method evaluates image sensor performance across multiple conversion gain parameters and brightness levels. By measuring SNR at different unit areas with varying brightness, the system identifies optimal conversion gain parameter combinations that prevent SNR dips, enabling dynamic adjustment of conversion gain settings based on measured performance characteristics.
3Measurement precision
If a centralized evaluation area with circular arrangement is used, then measurement precision of SNR dips is improved, but the ease of operation and setup time increase
Solution Approach 1:
The evaluation area is arranged in a circular pattern with unit areas distributed along the circumference. This circular geometry provides uniform angular distribution of different brightness levels, enabling comprehensive evaluation of SNR characteristics in all directions. The curved arrangement ensures consistent measurement conditions across different spatial positions, improving precision in detecting SNR dips that may vary with spatial location.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate evaluation and optimization of image sensor performance by identifying and addressing SNR dips, leading to improved image quality and settings adjustments that enhance the HDR mode performance.
Implementation Method 1
An image sensor may be a semiconductor-based sensor receiving light and generating an electrical signal in response to the received light
Data Source
AI summary
Methods of adjusting an image sensor may be provided. A test image of a test chart including an evaluation area may be obtained using the image sensor. The evaluation area may have a shape of a circle, wherein the evaluation area includes first through nth unit areas arranged in a rotational direction around the circle, and wherein each of the first through nth unit areas has a different level of brightness. A signal-to-noise ratio (SNR) may be calculated for each of the first through nth unit areas based on the test image of the test chart. A setting of the image sensor may be adjusted based on calculating the signal-to-noise ratio for each of the first through nth unit areas.