Image Processor Segmentation for Wide-FOV Storage Reduction
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Solution Overview
Problem
Surveillance cameras capture wide-FOV images, making it inconvenient for users to focus on specific regions of interest, and high-resolution images consume significant storage space and bandwidth, leading to high hardware costs.
Innovation Solution
An image processor that reduces the size of original images to generate a first image with a smaller resolution and crops the original image to create a second image corresponding to a region of interest, then combines these to produce an output image, allowing for efficient storage and transmission while maintaining detailed content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the surveillance camera operates in high resolution to provide clear images, then the image quality is improved, but the storage space and transmission bandwidth requirements increase significantly
Solution Approach 1:
The patent divides the original high-resolution image into multiple low-resolution sub-images, each covering a specific region of interest. This segmentation allows the system to maintain high image quality for critical areas while reducing overall data volume by processing multiple smaller regions instead of one large image.
Solution Approach 2:
The patent transitions from a single high-resolution 2D image to multiple low-resolution 2D sub-images that collectively represent the same spatial information. This dimensional transformation reduces the data burden while preserving essential visual details across different regions.
2Area of stationary object
If the surveillance camera captures wide-FOV images to monitor the entire space, then the coverage area is improved, but it becomes inconvenient for users to focus on specific regions of interest
Solution Approach 1:
The patent segments the wide-FOV image into multiple sub-images, each focusing on a specific region of interest. This allows users to easily navigate and examine particular areas without being overwhelmed by the entire wide-angle view, improving operational convenience while maintaining comprehensive coverage.
Solution Approach 2:
The patent applies different processing qualities to different regions of the image. Regions of interest receive focused attention with dedicated sub-images, while other areas are represented in the overall context. This local differentiation enhances user ability to focus on important areas while preserving awareness of the complete scene.
3Measurement precision
If the user manually adjusts the region of interest to observe detailed content, then the observation precision is improved, but information about other objects may be lost
Solution Approach 1:
The patent creates multiple sub-images that segment different regions of the original image, allowing users to examine specific areas in detail while maintaining access to other regions through the collection of sub-images. This prevents information loss by ensuring all areas are represented in the segmented output.
Solution Approach 2:
The patent makes the image processing system multi-functional by generating sub-images that serve both detailed examination purposes and comprehensive monitoring purposes. The same set of sub-images allows users to focus on specific regions while also providing an overview of the entire scene, eliminating the need to choose between detailed observation and complete coverage.
Data Source
AI summary
An image processor and an image combination method thereof are provided. The image processor includes a processing unit for performing the image combination method, and a storing unit for storing an original image and an output image. The image combination method includes the following steps. First, the original image is received from the storing unit. A first processing procedure scales down the original image to generate a first image. A second processing procedure crops the original image to generate a second image. The first image and the second image are combined to form and then be outputted the output image. Accordingly, the image processor and the image combination method are capable of providing the overview and local detailed content of the original image at the same time.