Image Encoding ROI Control via Motion Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image compression techniques face challenges in efficiently enhancing image quality in regions of interest (ROI) without unnecessary enhancement, especially when motion is small or when vegetation or water surfaces are present, leading to increased bit rates and reduced compression efficiency.
Innovation Solution
An image processing system that detects specific objects and moving objects in a moving image, setting a region of interest (ROI) based on their positions and performing encoding with higher quality parameters only when a moving object is detected within that ROI, using a smaller quantization parameter for enhanced image quality in relevant regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a user-set ROI is used to enhance image quality, then the image quality of the ROI is constantly enhanced, but the bit rate increases unnecessarily when there is no change in the video
Solution Approach 1:
The patent applies dynamics by switching between static user-set ROI and dynamic motion-detected ROI based on actual video content changes. The system dynamically adjusts which regions receive enhanced encoding quality, using motion detection to identify when and where quality enhancement is actually needed, thereby avoiding unnecessary bit rate increase during static scenes while maintaining quality when objects move.
Solution Approach 2:
The patent changes encoding parameters (quantization parameters) based on detected motion. When motion is detected in a region, the system adjusts the quantization parameter to provide higher quality encoding for that specific region, while maintaining lower quality encoding in static regions. This parameter adjustment is conditional and dynamic, resolving the contradiction between constant quality enhancement and bit rate efficiency.
2Manufacturing precision
If a dynamic ROI is used to enhance image quality based on motion detection, then the image quality is enhanced when there is a change in the video, but the ROI is not set if the motion is small, so the image quality of the required region is not enhanced
Solution Approach 1:
The patent merges user-set ROI (static interest regions) with motion-detected ROI (dynamic regions of change) into a unified ROI framework. By combining these two approaches, the system ensures that both stationary important regions and moving regions receive appropriate quality enhancement, regardless of whether the motion is large or small. This combination resolves the issue where small motions might be missed by pure motion detection.
Solution Approach 2:
The patent uses user-set ROI as a preliminary indication of important regions before motion detection is applied. This preliminary static ROI setting ensures that regions requiring quality enhancement are pre-identified, and then motion detection refines this by adding dynamically moving regions. This two-stage approach ensures that even small motions in pre-identified important regions are captured for quality enhancement.
3Manufacturing precision
If all dynamic ROIs are enhanced to ensure moving objects are captured, then the image quality of moving objects is improved, but the ROI becomes unnecessarily large when there is vegetation, a water surface, or the like that constantly moves
Solution Approach 1:
The patent applies local quality by differentiating between types of moving regions and applying quality enhancement selectively. Rather than enhancing all detected motion uniformly, the system uses object classification to identify whether motion corresponds to significant objects (people, vehicles) or natural elements (vegetation, water). Quality enhancement is then applied locally only to regions containing significant moving objects, reducing unnecessary ROI size while maintaining quality for important subjects.
Solution Approach 2:
The patent employs feedback mechanisms through object classification and analysis of motion patterns to determine whether detected motion represents a significant object requiring quality enhancement. By analyzing the type and characteristics of motion, the system provides feedback to the ROI selection process, adjusting which regions receive enhanced encoding based on whether the motion is meaningful (a person walking) or background noise (swaying trees), thereby optimizing ROI size.
Data Source
AI summary
An image processing apparatus comprises a specific object detection unit configured to detect a specific object from a moving image, a setting unit configured to set, in the moving image, based on a position of the specific object, a region of interest which is a region for performing an encoding process that produces a relatively higher image quality than in another region other than the region of interest, a moving object detection unit configured to detect a moving object from the moving image, and an encoding unit configured to perform an encoding process using, in the region of interest, an encoding parameter that can produce a relatively higher image quality than an encoding parameter used in the other region, when the moving object is detected in the region of interest.