AI Image Sensor ROI Resolution Processing
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Solution Overview
Problem
Traditional image sensors face a trade-off between high-resolution image processing and data communication bandwidth, leading to significant latency in transmitting high-resolution images due to limited network speeds, which is not efficiently addressed by existing technologies.
Innovation Solution
The proposed solution involves an AI-based image data processing method that reduces the resolution of image data from an image sensor, determines regions of interest (ROI) with varying priority levels, and modifies their resolution accordingly, allowing for efficient transmission through narrow bandwidth data communication links while maintaining key content specificity. This method includes a first resolution modification unit, a feature detection unit, a second resolution modification unit, and a data combination unit within an image sensor array to process and transmit image data effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution image data is transmitted through the data communication link, then image quality is maintained, but transmission latency increases significantly due to limited bandwidth
Solution Approach 1:
The image data is segmented into multiple priority levels based on AI-based object recognition. High-priority regions (containing important objects) are transmitted at full resolution, while low-priority regions are transmitted at reduced resolution or omitted entirely. This segmentation allows the system to maintain critical image quality while reducing overall data volume for transmission.
Solution Approach 2:
Different resolution qualities are applied to different regions of the image based on their importance. Regions containing important objects maintain high resolution, while other regions use lower resolution. This local quality differentiation optimizes the balance between image quality and transmission bandwidth utilization.
2Productivity
If the resolution of all image data is reduced to match bandwidth limitations, then transmission efficiency improves, but loss of important visual information occurs
Solution Approach 1:
AI-based object recognition is performed on the full-resolution image before transmission to identify important objects and their locations. This preliminary analysis enables the system to determine which regions require high-resolution transmission, ensuring that no important visual information is lost while optimizing overall transmission efficiency.
Solution Approach 2:
The system applies different quality levels locally to different image regions based on their importance. High-priority regions containing important objects are transmitted at full resolution, while low-priority regions use reduced resolution, thereby preserving essential visual information while improving transmission efficiency.
3Measurement precision
If AI-based object recognition is performed on full-resolution images, then accurate object detection is achieved, but processing time and computational resources increase
Solution Approach 1:
Instead of processing the entire full-resolution image for transmission, the system performs AI-based object recognition to identify only the critical regions requiring high-resolution transmission. This partial processing approach maintains accurate object detection while significantly reducing the time and computational resources required compared to processing the complete image.
Solution Approach 2:
The system extracts only the essential information (object locations and priorities) from the full-resolution image using AI recognition, rather than transmitting or processing the entire image. This extraction approach enables accurate object detection while minimizing processing time and resource consumption.
4Loss of information
If full-resolution image data is transmitted, then complete visual information is preserved, but data communication bandwidth is exceeded causing data overflow
Solution Approach 1:
The image data is divided into priority-based segments where only essential high-priority regions are transmitted at full resolution. This segmentation reduces the total data volume to fit within bandwidth constraints while preserving the most important visual information that would otherwise be lost in compressed transmissions.
Solution Approach 2:
The system transmits different quality levels for different image regions, with high-priority regions maintaining full resolution and low-priority regions using reduced resolution or being omitted. This approach preserves critical visual information while controlling overall data volume to prevent bandwidth overflow.
Data Source
AI summary
An image data processing method includes receiving first frame image data at a first resolution, reducing a resolution of the first frame image data to a second resolution, performing image recognition on the first frame image data to determine one or more regions of interest (ROI) and a priority level of each of the ROIs; receiving second frame image data, and extracting portions of the second frame image data corresponding to the one or more ROIs. The method further includes modifying a resolution of the portions of the second frame image data corresponding to the ROIs based on the priority level of the ROIs, reducing a resolution of the received second frame image data to the second resolution, and combining the resolution-modified portions of the second frame image data corresponding to the ROIs with the second frame image data at the second resolution to generate output frame image data.