Real-Time Image Adjustment via Dynamic LUT Selection
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Solution Overview
Problem
Low-performance imaging devices, such as webcams, face challenges in applying sophisticated image enhancements in real-time due to hardware and software limitations, particularly in choosing optimal color Look Up Tables (LUTs) that adapt to varying lighting and scene conditions.
Innovation Solution
A system comprising an imaging device and a controller that executes an image adjustment program to apply LUTs dynamically, using a calibration routine and database queries to adjust images in real-time, with a convolutional neural network for image processing and a calibration assistance device to collect iterative calibration data, ensuring optimal image adjustments based on device and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual LUT selection is performed, then optimal image enhancement can be achieved, but it is time-consuming and technically challenging for users
Solution Approach 1:
The system performs automatic LUT selection through calibration routines that capture test images, analyze device characteristics and lighting conditions, and select optimal LUTs without user intervention. The controller autonomously executes the calibration routine and applies adjustments based on detected conditions.
Solution Approach 2:
The system changes LUT parameters dynamically based on detected imaging conditions. Multiple LUTs are stored with different characteristics, and the system selects and applies the most appropriate LUT based on real-time analysis of device properties and environmental factors.
2Ease of operation
If automated LUT selection is attempted, then user effort is reduced, but it is difficult to automate due to wide variability of devices and conditions
Solution Approach 1:
The system tailors LUT selection to specific local conditions by analyzing individual device characteristics and environmental factors. Each calibration routine is customized based on the detected imaging device properties and lighting conditions, rather than applying a generic solution.
Solution Approach 2:
The calibration routine is designed to work across multiple device types and conditions. The system captures calibration data under various lighting scenarios and adapts the LUT selection process to handle different imaging devices, making the automated system universally applicable.
3Manufacturing precision
If a single LUT is applied for a particular condition, then image enhancement is achieved, but the LUT becomes sub-optimal when conditions change
Solution Approach 1:
The system dynamically selects and switches between multiple LUTs based on real-time detection of changing imaging conditions. The controller continuously monitors the video stream and recalibrates by selecting appropriate LUTs as lighting and scene conditions evolve during the live stream.
Solution Approach 2:
The system performs periodic recalibration during the live stream by detecting predetermined changes in imaging conditions. When changes are detected, the calibration routine is re-executed to select new appropriate LUTs, ensuring continuous optimal enhancement.
4Manufacturing precision
If sophisticated image enhancements are applied, then image quality is improved, but hardware and software limitations prevent practical application in real-time
Solution Approach 1:
The system extracts only the essential image enhancement operations needed for real-time processing. By focusing on LUT-based color space mapping rather than complex multi-step enhancements, the system achieves practical real-time performance while maintaining quality improvement.
Solution Approach 2:
The system uses computationally efficient LUTs that can be rapidly applied and discarded, rather than expensive complex enhancement algorithms. Multiple pre-computed LUTs are stored and quickly swapped based on conditions, enabling real-time processing on limited hardware.
Data Source
AI summary
Methods, system and devices for applying adjustments to images of a video stream are provided. An image device controller is configured to apply image adjustments to the images received from an imaging device, to map at least a portion of each of those images from one colour space to another in real-time. A calibration routine is executed that includes transmitting calibration data associated with the imaging device to an image adjustment engine. In response, the image adjustment engine processes that calibration data to determine a set of image adjustment instructions, and sends them to the controller. The controller receives the set of image adjustment instructions, and them to apply a customised mapping of colours to at least a portion of each image generated by the imaging device.