Adaptive 3A Algorithm Statistics Resolution for Computational Complexity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The computational complexity of 3A algorithms in digital cameras, which includes auto focus, auto exposure, and auto white balance, is high due to the need for detailed statistics, leading to increased power consumption and potential delays in processing, especially in variable imaging conditions, resulting in suboptimal image quality.
Innovation Solution
An adaptive technique is implemented to dynamically adjust the resolution of imaging statistics based on scene stability, using multi-scale representations and ISP hardware configurations to reduce computation complexity while maintaining image quality, allowing for real-time processing without skipping frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution statistics are used in 3A algorithms, then image quality is improved, but computation complexity increases
Solution Approach 1:
The patent applies dynamics by making the statistics resolution adaptive rather than fixed. The system dynamically adjusts the resolution level based on scene stability analysis, using high resolution when scenes are stable and low resolution when scenes are dynamic. This resolves the contradiction by making computation complexity variable while maintaining image quality where needed.
Solution Approach 2:
The patent changes the parameter of statistics resolution from a fixed high value to a variable value that adapts to scene conditions. By analyzing scene stability and adjusting the resolution parameter accordingly, the system maintains measurement precision (image quality) when necessary while reducing computation complexity when possible.
2Measurement precision
If high-resolution statistics are used in 3A algorithms, then image quality is improved, but power consumption increases
Solution Approach 1:
The system dynamically adjusts power consumption by varying the statistics resolution based on scene stability. During stable scenes, high-resolution statistics are processed which consumes more power but ensures quality. During dynamic scenes, low-resolution statistics are used reducing power consumption while maintaining adequate performance through adaptive control.
Solution Approach 2:
The patent changes the power consumption parameter by adapting the resolution level. When scene stability is high, the system uses higher resolution settings that consume more power for better quality. When scene stability is low, the system reduces resolution to lower power consumption, resolving the contradiction between quality and energy use.
3Measurement precision
If high-resolution statistics are used in 3A algorithms, then image quality is improved, but processing time increases
Solution Approach 1:
The patent applies dynamics by making processing time variable through adaptive resolution selection. The system analyzes scene stability and dynamically adjusts the statistics resolution, ensuring that high image quality is maintained when scenes are stable (allowing more processing time) while reducing processing time when scenes are dynamic by using lower resolution statistics.
Solution Approach 2:
The system changes the processing time parameter by adapting the resolution level based on scene conditions. When scene stability is high, the system can allocate more processing time to high-resolution statistics for better quality. When scene stability is low, the system reduces resolution to decrease processing time, resolving the contradiction between quality and time.
4Measurement precision
If full-resolution statistics are processed, then image quality is maintained, but device power consumption increases
Solution Approach 1:
The patent makes the statistics processing resolution dynamic rather than static. The system continuously analyzes scene stability and adjusts the resolution level accordingly, using full-resolution statistics only when scene stability warrants it. This dynamic adaptation resolves the contradiction by matching power consumption to actual quality requirements in real-time.
Solution Approach 2:
The system changes the power consumption parameter by adapting the statistics resolution to scene stability. When stability is high, full-resolution processing is used maintaining quality with higher power consumption. When stability is low, resolution is reduced to lower power consumption while maintaining adequate quality through adaptive control.
Data Source
AI summary
Technology described herein provides for adapting statistics resolution for 3A algorithms. The technology is to analyze a scene from a plurality of input images to determine a stability score, determine a target imaging statistics resolution based on the stability score, and calculate, using imaging statistics corresponding to the target imaging statistics resolution, an auto exposure parameter and/or an auto white balance parameter. In one aspect, the technology is to generate a multi-scale statistics set including a plurality of sets of imaging statistics, select the target imaging statistics resolution from a predetermined set of resolutions, and select, from the plurality of sets of imaging statistics, a set of imaging statistics corresponding to the target imaging statistics resolution. In another aspect, the technology is to compute the target imaging statistics resolution based on the stability score and a maximum imaging statistics resolution for the computing device.