Composite ISP with Pixel-Level ML Tuning for Scene Adaptation
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Solution Overview
Problem
Traditional image signal processors (ISPs) require extensive manual tuning and are limited to static settings, leading to inefficiencies in image processing and high resource consumption, especially in devices with limited battery life and computational resources.
Innovation Solution
A composite image signal processor (ISP) that combines pre-tuned components with trained machine learning models to dynamically adjust settings for each pixel, optimizing image processing based on scene-specific conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional ISPs use pre-tuned parameters with discrete functional blocks, then the processing pipeline is simple and reliable, but the system lacks adaptability to different scenes and requires extensive manual tuning
Solution Approach 1:
The patent applies dynamics by replacing static pre-tuned parameters with dynamic machine learning models that automatically adapt to different scenes. The ML models process image data and generate scene-specific tuning parameters in real-time, enabling the ISP to dynamically adjust its behavior based on the captured scene rather than relying on fixed pre-configured settings.
Solution Approach 2:
The patent substitutes the mechanical/manual tuning system with an automated machine learning-based system. Instead of manual engineering tuning or simple discrete functional blocks, the system uses trained neural networks to automatically determine optimal processing parameters, replacing mechanical adjustment mechanisms with intelligent automation.
2Productivity
If traditional ISPs use a limited set of static tuning settings, then the device complexity is low, but the processing efficiency and image quality are reduced
Solution Approach 1:
The patent applies self-service by enabling the ISP to automatically determine its own optimal processing parameters through machine learning models. The system processes image data through ML models that generate appropriate tuning settings without requiring external manual configuration, allowing the system to self-optimize based on the captured scene characteristics.
Solution Approach 2:
The patent changes parameters from fixed static values to dynamic values generated by machine learning models. The ML models output scene-specific parameter settings that vary based on the input image data, enabling continuous adaptation and optimization of processing parameters rather than being limited to a predetermined finite set of configurations.
3Reliability
If fully machine learning-based ISPs are used, then adaptability and image quality improve, but computational resources and energy consumption increase
Solution Approach 1:
The patent segments the ISP into distinct functional components: machine learning models for scene analysis and parameter generation, and traditional discrete functional blocks for executing the processing operations. This segmentation allows the system to leverage the strengths of both approaches - ML for adaptability and traditional blocks for efficient, deterministic processing - thereby reducing overall computational energy consumption compared to fully ML-based systems.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between the image sensor and the processing functional blocks. The ML models analyze the captured scene and generate appropriate tuning parameters that guide the traditional processing blocks, serving as an intelligent mediator that enables adaptability without requiring all processing operations to be performed through computationally intensive ML inference.
4Manufacturing precision
If manual tuning of ISP parameters is performed, then the processing precision for specific scenes is optimized, but the development time and cost increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on diverse image datasets before the actual image processing task. The ML models are trained in advance to recognize scene characteristics and generate appropriate tuning parameters, so that when new images are captured, the system can immediately leverage the pre-learned knowledge without requiring time-consuming manual re-tuning for each specific application.
Data Source
AI summary
Systems and techniques are described for image processing. An imaging system can include an image sensor that captures image data. An image signal processor (ISP) of the imaging system can demosaic the image data. The imaging system can input the image data into one or more trained machine learning models, in some cases along with metadata associated with the image data. The one or more trained machine learning models can output settings for a set of parameters of the ISP based on the image data and/or the metadata. The imaging system can generate an output image by processing the image data using the ISP, with the parameters of the ISP set according to the settings. Each pixel of the pixels of the image data can be processed using a respective setting for adjusting a corresponding parameter. The parameters of the ISP can include gain, offset, gamma, and Gaussian filtering.