Security Camera Image Processing with Important Area Extraction
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Solution Overview
Problem
Existing image processing technologies uniformly compress or process whole image data, leading to increased resource requirements and difficulty in reducing information without degrading useful information, particularly in sensing applications where only specific information is needed.
Innovation Solution
A security camera system with an important area extraction circuit that divides the image frame into important and unimportant areas, transmitting only the important area data to the image signal processor, along with additional information generation and synthesis circuits to reduce data processing load and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If uniform compression is applied to the whole image data, then the amount of information is reduced, but the useful information degrades
Solution Approach 1:
The image frame is divided into multiple regions of interest (ROIs) and non-ROI areas. Different compression ratios are applied to different regions: higher compression to non-ROI areas and lower compression or no compression to ROI areas. This segmentation allows the system to reduce overall data volume while preserving the quality of useful information in critical regions.
Solution Approach 2:
Different quality levels are assigned to different parts of the image based on their importance. Regions containing useful information (such as areas with motion detection, face detection, or user-defined important areas) are processed with higher quality settings, while less important areas use lower quality settings. This local differentiation resolves the contradiction by maintaining useful information quality where needed while reducing overall data volume.
2Manufacturing precision
If resolution improvement is implemented, then image quality improves, but the size of data to be handled becomes extremely larger
Solution Approach 1:
The high-resolution processing is selectively applied only to regions of interest rather than the entire image. The image is segmented into ROI and non-ROI areas, with high resolution maintained only in ROI areas where useful information is present. This segmentation allows the system to achieve high image quality for critical regions while keeping the overall data size manageable.
Solution Approach 2:
Different resolution levels are assigned to different parts of the image. High resolution is applied locally to regions containing useful information, while lower resolution is used for the remainder of the image. This local quality approach ensures that image quality is improved where it matters most without proportionally increasing the total data size.
3Adaptability or versatility
If uniform processing is applied to the whole image, then versatility is maintained, but the efficiency in sensing applications decreases
Solution Approach 1:
The system dynamically adjusts processing parameters based on the content and application requirements. Different sensing applications (such as face detection, motion detection, or general monitoring) can trigger different processing modes, with the system adapting its region identification and compression strategies in real-time. This dynamic approach maintains versatility across different applications while optimizing efficiency for each specific use case.
Solution Approach 2:
The system changes processing parameters such as compression ratio, resolution, and region boundaries based on application-specific requirements. Different applications can specify different parameters for what constitutes a region of interest, allowing the same hardware to efficiently serve multiple sensing applications with optimized processing for each.
Data Source
AI summary
A security camera system capable of reducing an amount of information without degrading useful information is provided. The security camera system includes a pixel array, an image signal processor that performs various types of image processing on raw data including each pixel value from the pixel array, and a pre-stage processing unit arranged between the pixel array and the image signal processor. The pre-stage processing unit includes an important area extraction circuit that receives raw data from the pixel array with respect to each frame and divides an effective pixel area of the frame into an important area and an unimportant area, and transmits the raw data of the important area to the image signal processor.