Image Post-Processor Downsampling for Bandwidth Reduction
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Solution Overview
Problem
Current image processing systems face challenges in efficiently analyzing high-resolution images in real-time, particularly in applications like autonomous vehicles and facial recognition, due to high system bandwidth requirements and limited processing capacity, which hinders the detection and extraction of regions of interest.
Innovation Solution
An image processing apparatus and method that involves an image processor generating high-resolution images, an image post-processor downsampling these images to a lower resolution for analysis by a computer vision engine, and using image croppers to extract regions of interest from the original high-resolution images, reducing system bandwidth and enhancing processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution images are processed directly, then image quality and detection precision are improved, but system bandwidth requirements and processing capacity increase significantly
Solution Approach 1:
The patent divides the image processing task into two segments: first, downscale the entire high-resolution image to a lower resolution for initial region of interest detection; second, extract only the identified regions of interest from the original high-resolution image for detailed analysis. This segmentation allows the system to reduce bandwidth consumption while maintaining detection precision for important regions.
Solution Approach 2:
The patent extracts only the necessary parts (regions of interest) from the full high-resolution image after identifying them through analysis of a downscaled version. This extraction principle reduces the amount of data that needs to be processed and transmitted, thereby reducing system bandwidth requirements while preserving the precision needed for detailed analysis of critical regions.
2Measurement precision
If high-resolution images are analyzed in real-time, then detection accuracy is improved, but processing capacity and time are insufficient
Solution Approach 1:
The patent segments the processing workflow into two stages: first processing the downscaled image for rapid region identification, then processing only the extracted regions of interest from the original high-resolution image. This segmentation enables real-time processing by reducing the total computational load while maintaining accuracy for critical regions.
Solution Approach 2:
The patent extracts only the necessary regions of interest from the full image for detailed processing, rather than analyzing the entire high-resolution image. This extraction significantly reduces processing capacity requirements and enables real-time analysis while maintaining detection accuracy for important objects and regions.
3Loss of information
If the entire high-resolution image is processed, then complete information is maintained, but system resources and bandwidth are consumed excessively
Solution Approach 1:
The patent segments the image processing into two phases: first, analyze the downscaled image to identify regions of interest; second, extract only those regions from the original high-resolution image. This segmentation maintains information completeness for critical regions while reducing overall system resource consumption by avoiding processing of the entire high-resolution image.
Solution Approach 2:
The patent extracts only the necessary regions of interest from the full high-resolution image based on analysis of a downscaled version. This extraction approach maintains information completeness for important regions while significantly reducing system resource consumption and bandwidth requirements by processing only the extracted regions rather than the entire image.
Data Source
AI summary
The present disclosure provides an image processing apparatus and system which downscales an image which is generated from data provided by a sensor. The downscaled image is then analyzed to determine the location of one or more regions of interest in the image. The regions of interest can then be cropped from the original image and those cropped regions of interest processed by a computer vision engine.