Dynamic Image Quality Optimization via Scene Content Analysis
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Solution Overview
Problem
Image sensors, particularly CMOS sensors, face challenges in optimizing image quality due to noise and blur issues, especially in low light conditions, where there is a tradeoff between noise reduction and power consumption, and residual fixed pattern noise remains after non-uniformity correction.
Innovation Solution
An apparatus and method that utilize a digital processor to analyze scene content, adjust sensor parameters, and align and combine frames to optimize image quality, reducing noise and blur while minimizing power consumption, by dynamically adjusting frame combination parameters based on scene content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the sensor operates at higher frame rates to reduce motion blur, then image sharpness is improved, but power consumption increases and pixel circuitry complexity increases
Solution Approach 1:
The system dynamically adjusts the frame rate and frame combination parameters based on real-time scene content analysis. When motion is detected, the system increases frame rate to reduce blur; when static scenes are detected, it reduces frame rate to save power. This dynamic adaptation resolves the contradiction between sharpness and power consumption.
Solution Approach 2:
The system changes operational parameters (frame rate, integration time, frame combination weights) based on scene characteristics. By analyzing scene content and adjusting parameters accordingly, the system achieves optimal image quality while minimizing power consumption, resolving the tradeoff between sharpness and energy use.
2Manufacturing precision
If the frame integration time is reduced to reduce motion blur, then image sharpness is improved, but signal strength relative to noise decreases
Solution Approach 1:
The system combines multiple frames through alignment and weighted averaging to enhance signal strength. By merging information from multiple frames, the system achieves both reduced motion blur (through frame alignment) and improved signal-to-noise ratio (through signal accumulation), resolving the tradeoff between sharpness and signal strength.
Solution Approach 2:
The system continuously processes frames and combines them to maintain optimal image quality. Through continuous frame alignment and combination, the system sustains both sharpness and signal strength, eliminating the need to choose between these two parameters.
3Measurement precision
If non-uniformity correction is applied to reduce fixed pattern noise, then image quality is improved, but residual fixed pattern noise remains especially in low light conditions
Solution Approach 1:
The system continuously combines multiple frames, and since fixed pattern noise is consistent across frames while signal varies, the continuous combination process progressively reduces residual fixed pattern noise. This continuous action enables the system to overcome the limitations of single-frame correction and achieve superior noise reduction in low light conditions.
Data Source
AI summary
A method and apparatus for optimizing image quality based on scene content comprising a sensor for generating a sequence of frames where each frame in the sequence of frames comprises content representing a scene and a digital processor, coupled to the sensor, for performing scene content analysis and for establishing a window defining a number of input frames from the sensor and processed output frames, and for aligning and combining the number of frames in the window to form an output frame, wherein sensor parameters and frame combination parameters are adjusted based on scene content.