Object-Categorized ISP Settings for Mixed-Scene Image Processing
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Solution Overview
Problem
Traditional image signal processors (ISPs) are tuned once during manufacturing and apply the same settings to all images, which results in suboptimal performance for diverse scenes, leading to unwanted effects when multiple types of objects are present.
Innovation Solution
An image capture and processing device partitions raw image data into different regions based on object detection, applying distinct ISP settings for each region, such as enhancing sharpness in hair and reducing noise in skin, with adjustments based on confidence levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image processing is performed on all captured images without categorization, then processing completeness is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the image processing workflow by categorizing images into different types (e.g., product images, packaging images, background images) using machine learning models. Different processing pipelines are then applied to different categories, allowing selective and optimized processing rather than uniform processing of all images.
Solution Approach 2:
The patent changes the processing parameters dynamically based on image category. For example, product images may receive detailed quality checks and multiple angle validations, while background images may undergo simpler processing. This adaptive parameter adjustment reduces overall processing time while maintaining necessary quality standards.
2Manufacturing precision
If detailed image quality checks are performed on all images, then quality control is improved, but processing speed decreases
Solution Approach 1:
The patent applies local quality control by performing detailed checks only on specific regions or aspects of images based on their category. For product images, detailed checks focus on product defects and positioning. For packaging images, checks focus on label accuracy and barcodes. This localized approach maintains quality control where needed while reducing unnecessary processing elsewhere.
Solution Approach 2:
The patent implements partial action by applying different levels of quality checking based on image importance and category. Critical product images receive exhaustive quality checks, while less critical images receive streamlined processing. This selective approach ensures necessary quality control while improving overall processing throughput.
3Productivity
If multiple processing pipelines are used for different image types, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary classification layer that automatically categorizes incoming images using machine learning models. This intermediary component directs images to appropriate processing pipelines, managing the complexity of multiple pipelines through intelligent routing rather than requiring manual configuration or complex switching logic.
Solution Approach 2:
The patent implements feedback mechanisms where processing results and image characteristics are continuously analyzed to refine the categorization model and optimize pipeline selection. This feedback loop allows the system to adapt to new image types and processing requirements, managing complexity through learning and adaptation rather than rigid structural complexity.
Data Source
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AI summary
Examples are described for applying different settings for image capture to different portions of image data. For example, an image sensor can capture image data of a scene and can send the image data to an image signal processor (ISP) and a classification engine for processing. The classification engine can determine that a first object image region depicts a first category of object, and a second object image region depicts a second category of object. Different confidence regions of the image data can identify different degrees of confidence in the classifications. The ISP can generate an image by applying a different settings to the different portions of the image data. The different portions of the image data can be identified based on the object image regions and confidence regions.