AI-Aware ISP Control for Uniform Images and Better Inference
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Solution Overview
Problem
Existing image signal processors (ISPs) adjusted by human operators result in non-uniform image data for artificial neural network models, leading to reduced inference accuracy, and existing methods for improving this accuracy require separate processors or repetitive adjustments without considering neural network characteristics.
Innovation Solution
A method and apparatus that select an inference accuracy profile based on image data characteristics to control the ISP parameters, using image characteristics like histograms and SNR to improve preprocessing and neural network inference accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an image signal processor adjusted by human operators is used, then image processing can be performed with operator expertise, but the image data becomes non-uniform and inference accuracy of artificial neural network models deteriorates
Solution Approach 1:
The system automatically determines image characteristics and selects appropriate processing parameters without requiring human operator expertise. The image signal processor self-adjusts based on detected characteristics (brightness, contrast, noise levels) to produce uniform data suitable for neural network inference, eliminating dependency on operator skills while maintaining processing quality
Solution Approach 2:
The system dynamically changes processing parameters based on detected image characteristics. By adjusting parameters such as gamma correction levels, noise reduction strength, and contrast enhancement based on actual image analysis, the system transforms diverse input images into standardized output data that improves inference accuracy across different operating conditions
2Reliability
If separate processors are used to preprocess images for neural networks, then inference accuracy can be improved, but device complexity and processing time increase
Solution Approach 1:
The patent merges the image processing functions with neural network inference capabilities into a single integrated system. The image signal processor incorporates neural network awareness, combining traditional ISP functions with inference optimization in one device, thereby improving accuracy without adding separate processors or increasing overall system complexity
Solution Approach 2:
The image signal processor is designed to perform multiple functions: traditional image processing (demosaicing, noise reduction) plus neural network-specific preprocessing (characteristic detection, parameter selection). This multi-functional approach eliminates the need for dedicated separate processors while maintaining improved inference accuracy
3Adaptability or versatility
If image signal processor settings are repeatedly adjusted for new images, then processing flexibility is maintained, but processing time and computational cost increase
Solution Approach 1:
The system performs preliminary analysis of image characteristics before final processing. By pre-detecting characteristics such as brightness distribution, contrast levels, and noise patterns, the system selects optimal processing parameters in advance, enabling rapid processing of new images without time-consuming iterative adjustments and maintaining both flexibility and speed
Data Source
AI summary
A control method of an image signal processor for an artificial neural network may be configured to include a step of acquiring an image, a step of determining at least one image characteristic data corresponding to the image, and a step of determining an image correction parameter (SFR preset) for improving an inference accuracy of an artificial neural network model based on the at least one of image characteristic data and an inference accuracy profile of an artificial neural network model.