Image Frame Segmentation for Motion-Aware Blur Effects
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Solution Overview
Problem
Existing electronic devices struggle to effectively generate blur effects in images, particularly when the image background contains contrasting information, leading to undesired background features being highlighted, which affects the overall user experience.
Innovation Solution
A method and electronic device that segment an image frame into multiple segments, determine the foreground and background regions, detect motion and static information, and automatically apply motion type and static type blur effects accordingly to generate a media file with enhanced blur effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single type of blur filter (e.g., Gaussian blur) is applied to the entire image, then the processing is simple and fast, but the blur effect is ineffective when the image background has contrasting information and undesired background features are highlighted
Solution Approach 1:
The image is divided into multiple segments or regions, allowing different blur filters to be applied to different areas. This segmentation enables the system to handle complex backgrounds with contrasting information by treating each region independently, thus improving blur effect quality without uniformly increasing processing complexity across the entire image.
Solution Approach 2:
Different types of blur filters are applied to different regions of the image based on local characteristics. For example, motion blur is applied to regions with moving objects while static blur is applied to background regions, creating locally optimized blur effects that enhance overall image quality while maintaining reasonable processing complexity.
2Reliability
If multiple blur filters are applied to different regions of the image, then the blur effect quality is improved, but the processing complexity increases
Solution Approach 1:
The system dynamically selects and applies appropriate blur filters based on real-time analysis of image content and motion detection. This dynamic approach allows the processor to efficiently determine which regions require which type of blur, optimizing processing efficiency while maintaining high blur effect quality through context-aware filter selection.
Solution Approach 2:
The system changes blur parameters (such as blur type, intensity, and kernel size) based on detected motion information and region characteristics. By adapting parameters to local image content, the system achieves high-quality blur effects while avoiding unnecessary computational overhead in regions where simple blur suffices.
3Reliability
If motion blur is applied to all regions, then motion information is preserved, but static regions lose detail and sharp features remain visible
Solution Approach 1:
Motion blur is applied selectively only to regions containing moving objects, while static regions receive different treatment (such as static blur or no blur). This local differentiation preserves motion information in dynamic regions while maintaining detail and sharpness in static regions, resolving the contradiction between motion representation and detail preservation.
4Manufacturing precision
If static blur is applied to all regions, then image detail is preserved, but motion information is lost and motion blur effects are not captured
Solution Approach 1:
The system dynamically adjusts the blur type applied to each region based on detected motion. Static regions receive static blur to preserve detail, while moving regions receive motion blur to accurately represent motion. This dynamic adaptation ensures both image detail preservation and reliable motion representation without losing motion information.
Data Source
AI summary
A method for producing a media file with a blur effect in an electronic device is provided. The method includes segmenting an image frame into a plurality of segments. Further, the method includes determining at least one segment from the plurality of segments comprising one of a foreground Region of Interest (ROI) and a background region of the ROI and detecting whether one of the foreground region of the ROI and the background region of the ROI comprises motion information and static information. Further, the method includes automatically applying a motion type blur effect and/or a static type blur effect on one of the foreground region of the ROI, and the background region of the ROI. The method includes generating the media file based on the applied the motion type blur effect and the static type blur effect and storing the media file.