Mobile Image Blurring Algorithm Selection for Shallow Depth-of-Field
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Solution Overview
Problem
Mobile terminals struggle to consistently produce high-quality shallow depth-of-field images due to inconsistent performance in various photographing environments, leading to issues like decreased brightness and contrast in certain lighting conditions, which limits user satisfaction.
Innovation Solution
Implementing a method and apparatus in mobile terminals that utilize multiple preset blurring algorithms to simulate professional camera lens effects, allowing for automatic or user-selected optical lens effects based on photographing environment parameters, thereby enhancing image quality and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single blurring algorithm is used to simulate shallow depth-of-field effect, then the device complexity is reduced, but the image quality becomes inconsistent across different photographing environments
Solution Approach 1:
The patent segments the image processing task by dividing the photographing environment into different scenarios (e.g., bright environments, dim environments) and assigns different blurring algorithms to each scenario. This segmentation allows the system to maintain image quality consistency across various environments while managing complexity through conditional selection rather than universal complexity
Solution Approach 2:
The patent changes the parameter of algorithm selection based on environment parameters (brightness, contrast). By detecting photographing environment parameters and selecting algorithms accordingly, the system adapts to different conditions without requiring a single complex algorithm to handle all scenarios, thus improving reliability while controlling overall system complexity
2Reliability
If multiple blurring algorithms are implemented to handle different photographing environments, then the image quality improves, but the device complexity increases
Solution Approach 1:
The patent implements a dynamic algorithm selection mechanism that adjusts the blurring algorithm based on real-time detection of photographing environment parameters. This dynamic approach allows the system to use multiple algorithms when needed while maintaining simplicity in each individual algorithm, balancing image quality improvement with manageable system complexity through adaptive selection
Solution Approach 2:
The patent performs preliminary detection of photographing environment parameters before selecting the blurring algorithm. By pre-assessing conditions such as brightness and contrast levels, the system can choose the most appropriate algorithm in advance, avoiding the need for complex real-time adjustments during the actual blurring process and thus managing system complexity more effectively
3Adaptability or versatility
If environment-based algorithm selection is implemented, then the adaptability to different photographing conditions improves, but the processing time increases
Solution Approach 1:
The patent performs environment parameter detection and algorithm selection as preliminary actions before the actual image blurring process. By determining the appropriate algorithm in advance based on detected conditions, the system minimizes processing time during the critical blurring operation while still maintaining high adaptability to different photographing environments
Solution Approach 2:
The patent segments the processing workflow into distinct phases: environment detection, algorithm selection, and execution. This segmentation allows the system to efficiently manage adaptability by making decisions in the detection and selection phases, while the execution phase focuses solely on applying the chosen algorithm, thereby reducing overall processing time through structured workflow optimization
Data Source
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AI summary
A method for processing an image and a mobile terminal. The method includes: obtaining (S110) a lens effect instruction, and determining a target blurring algorithm from a plurality of preset blurring algorithms based on the lens effect instruction, in which the blurring algorithms are algorithms for simulating optical lens effects; and obtaining (S120) a target image by blurring an image to be processed based on the target blurring algorithm.