Image Processing Apparatus ROI Encoding Moving Objects
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing methods fail to effectively enhance image quality within a Region of Interest (ROI) due to inadequate ROI setting, leading to unnecessary image quality enhancement and reduced compression efficiency, especially in scenarios with moving objects or backgrounds like vegetation or water surfaces.
Innovation Solution
An image processing apparatus that sets a user-defined ROI and dynamically adjusts image quality by detecting moving objects within the ROI, enhancing image quality only when necessary, using different quantization parameters for inside and outside the ROI to optimize compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire moving object region is set as the ROI, then the ROI includes all moving objects, but the ROI becomes larger than necessary when vegetation or water surfaces exist
Solution Approach 1:
The patent applies local quality by differentiating between different types of moving regions within the image. Instead of treating all moving objects uniformly, the system identifies and excludes regions with constant movement patterns (vegetation, water surfaces) from the ROI, while maintaining ROI status for regions with meaningful movement (people, vehicles). This allows different parts of the moving object region to have different quality treatments based on their specific characteristics.
Solution Approach 2:
The patent segments the moving object region into multiple sub-regions based on movement characteristics. By dividing the overall moving region and analyzing each segment's movement pattern, the system can selectively include or exclude specific segments from the ROI, thereby reducing unnecessary ROI coverage while maintaining detection reliability for important objects.
2Adaptability or versatility
If different ROI setting techniques are selected based on user selection or scene analysis, then the ROI can be adapted to different scenes, but the ROI is not set adequately in some cases
Solution Approach 1:
The patent implements feedback by continuously monitoring movement patterns within detected regions and using this information to refine and adjust the ROI setting. The system analyzes movement characteristics, compares them against known patterns of constant movement (vegetation, water), and dynamically adjusts the ROI boundaries accordingly, creating a self-correcting mechanism that improves both adaptability and accuracy.
Solution Approach 2:
The patent applies dynamics by making the ROI setting process adaptive and changeable based on real-time scene analysis. Rather than using static or pre-defined ROI regions, the system continuously updates the ROI based on detected movement patterns, allowing the ROI to dynamically adjust to different scenes and conditions while maintaining high accuracy.
3Reliability
If image quality enhancement is performed for the entire ROI, then all regions within the ROI are enhanced, but unnecessary image quality enhancement occurs when no moving objects are present
Solution Approach 1:
The patent applies partial action by performing image quality enhancement only on specific sub-regions within the ROI where meaningful moving objects are detected, rather than enhancing the entire ROI uniformly. This selective enhancement approach reduces unnecessary processing and bandwidth consumption in regions with constant movement patterns while maintaining high image quality for regions containing important objects.
Data Source
AI summary
An image processing apparatus sets a region of interest for an image frame of a moving image captured by a capturing unit, based on an operation by a user, detects a moving object region in the image frame, determines whether at least part of the detected moving object region is contained in the region of interest or not, and, in a case where at least part of the detected moving object region is determined to be contained in the region of interest, performs encoding such that the entire region of interest becomes higher in image quality than an outside of the region of interest.