Stereoscopic Exposure Control via Texture-Signal-to-Noise Ratio
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Solution Overview
Problem
Existing automatic exposure control methods for stereoscopy-based depth sensing systems are suboptimal as they rely on brightness criteria suitable for human vision, which does not effectively address the texture-to-noise ratio requirements for depth quality, leading to compromised depth quality and increased power consumption.
Innovation Solution
A novel automatic exposure control method that calculates the texture-signal-to-noise ratio (TSNR) metric using spectral and variance-to-mean methods, allowing for separate handling of block and image saturation, and implementing a control loop to determine optimal exposure duration for improved depth quality while minimizing exposure time and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exposure time is increased to improve texture-to-noise ratio and depth quality, then depth quality is improved, but power consumption increases and motion blur occurs
Solution Approach 1:
The system performs preliminary analysis of the captured image to evaluate texture-to-noise ratio and saturation levels before determining the final exposure time. This allows the system to pre-assess whether increasing exposure would benefit depth quality or would cause saturation, enabling optimal exposure selection that avoids unnecessary power consumption while maintaining depth quality
Solution Approach 2:
The system implements a feedback mechanism where the quality of captured images (texture-to-noise ratio, saturation levels) is continuously evaluated, and exposure time is dynamically adjusted based on this feedback. The control loop monitors image quality metrics and modifies exposure parameters in real-time to optimize depth quality while minimizing power consumption and avoiding motion blur
2Measurement precision
If exposure time is increased to improve texture-to-noise ratio, then depth quality is improved, but image saturation occurs and depth quality deteriorates
Solution Approach 1:
The system performs preliminary evaluation of saturation levels by analyzing the captured image before finalizing exposure time. This preliminary action allows the system to identify regions that would become saturated with increased exposure and adjust exposure parameters accordingly, preventing saturation before it occurs
Solution Approach 2:
The system uses feedback from image quality metrics including saturation levels to dynamically adjust exposure time. When saturation is detected or predicted, the control loop reduces exposure time to prevent further saturation, maintaining optimal depth quality without allowing harmful saturation to occur
3Use of energy by moving object
If exposure time is decreased to reduce power consumption and motion blur, then power consumption is reduced, but texture-to-noise ratio decreases and depth quality deteriorates
Solution Approach 1:
The system performs preliminary assessment of the captured image quality, evaluating texture-to-noise ratio and other depth quality metrics before determining exposure time. This allows the system to identify cases where increased exposure would significantly improve depth quality versus cases where current exposure is already sufficient, avoiding unnecessary power consumption while maintaining depth quality
Solution Approach 2:
The system implements feedback control where exposure time is dynamically adjusted based on real-time evaluation of depth quality metrics. When the system detects that texture-to-noise ratio is insufficient for acceptable depth quality, it increases exposure time; when quality metrics are sufficient, it reduces exposure time to minimize power consumption, creating an optimal balance between power efficiency and depth quality
4Illumination intensity
If traditional brightness-based autoexposure is used to achieve correct image brightness, then image brightness is optimized for human vision, but texture-to-noise ratio for depth sensing is not optimized
Solution Approach 1:
Instead of optimizing exposure for brightness (the traditional approach), the system inverts the objective by optimizing exposure specifically for texture-to-noise ratio and depth quality metrics. The control algorithm evaluates image quality based on texture content and noise levels rather than average brightness, fundamentally changing the optimization criterion to match the actual needs of depth sensing applications
Solution Approach 2:
The system changes the key parameter used for exposure control from brightness/intensity to texture-to-noise ratio. By introducing new quality metrics that specifically measure texture content and noise levels, the system transforms the exposure optimization problem from a brightness-based task to a depth quality-based task, enabling optimal exposure selection for machine vision depth sensing
Data Source
AI summary
A method and a stereoscopic apparatus configured to determine an exposure time period for capturing images. The apparatus comprises at least one image capturing device for capturing pairs of images; a processor configured to: calculate a texture-signal-to-noise ratio (TSNR) metric based on information derived from a pair of captured images; calculate an image saturation metric based on that pair of captured images; calculate a value for an exposure duration that will be implemented by the at least one image capturing device when another pair of images are captured; provide the value of the calculated exposure time period to each at least one image capturing device; and wherein the at least one image capturing device is configured to capture at least one image of the target while implementing each the respective calculated value of the exposure time period provided to the corresponding one of the at least one image capturing device.