Mobile Image Motion Blur Using Sensor-Driven Blur Kernels
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Solution Overview
Problem
Existing methods for adding motion blur to images on mobile devices lack interactivity and control over the blur effect, limiting the ability to dynamically adjust blur direction and intensity based on device motion and user preferences.
Innovation Solution
A system and method for mobile devices that utilize sensor data to measure device motion and apply blur effects interactively, combining metadata and device motion to control the direction and intensity of blur on images, allowing users to selectively blur foreground or background elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If motion blur is applied to moving objects in a scene, then the visual effect of motion is enhanced, but the processing time and computational resources required increase significantly
Solution Approach 1:
The system pre-calculates and stores motion blur parameters for objects based on their motion characteristics before rendering the final scene. By preparing motion blur data in advance during object processing, the system avoids computationally intensive calculations during the final rendering stage, thus reducing processing time while maintaining visual quality.
Solution Approach 2:
The motion blur processing is divided into separate stages: first processing individual objects independently to extract motion parameters, then applying blur effects during composition. This segmentation allows parallel processing of multiple objects and optimizes the rendering pipeline efficiency.
2Illumination intensity
If motion blur is applied to all objects in a scene, then the overall motion effect is improved, but the computational complexity and processing overhead increase
Solution Approach 1:
The system applies motion blur selectively based on local characteristics of each object. Motion parameters such as velocity, acceleration, and direction are calculated individually for each object, allowing the blur effect to be applied only where motion is detected. This avoids unnecessary processing of static or minimally moving objects, reducing overall computational complexity.
Solution Approach 2:
The system applies motion blur at appropriate levels of detail based on object importance and motion significance. For objects with minimal motion or low visual importance, reduced blur processing is applied, while objects with significant motion receive full processing. This partial action approach optimizes the balance between visual quality and processing complexity.
3Measurement precision
If motion parameters are calculated for each object individually, then the accuracy of motion blur is improved, but the processing time increases
Solution Approach 1:
Motion parameters for each object are pre-calculated during the object processing stage before final scene composition. By extracting velocity, acceleration, and direction data in advance, the system achieves accurate motion characterization without performing time-consuming calculations during the critical rendering phase.
Solution Approach 2:
The system maintains continuous tracking of object motion parameters throughout the animation sequence, updating blur parameters incrementally as objects move between frames. This continuous action approach preserves motion accuracy while avoiding redundant calculations by building upon previous frame data.
Data Source
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AI summary
Novel methods and systems are described for providing interactive motion blur on an image by motion inputs from movements of the mobile device displaying the image. The device can process the motion blur by modules providing motion blur parameter estimation, blur application, and image composition based on metadata and a baseline image from the encoder. A pre-loaded filter bank can provide blur kernels for blur application.