Blur Artifact Calculation Using Edge Detection and SML Focus Identification
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Solution Overview
Problem
Existing methods for calculating blur artifacts in videos are inaccurate, particularly for objects with low depth of field, and fail to identify focused areas, leading to inefficient removal of blur artifacts.
Innovation Solution
A method and system that detect edges, calculate edge gradients, and use the Sum of Modified Laplacian (SML) method to identify focused areas, determining blurred edge pixels and calculating the blur artifact as a ratio of blurred to total edge pixels, with predefined thresholds for edge spread and SML values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing edge detection techniques are used to calculate blur artifact, then the calculation can be performed, but the accuracy is poor for objects with low depth of field
Solution Approach 1:
The patent applies local quality by differentiating between focused and defocused regions using focus detection mechanisms. The system calculates blur artifact separately for different spatial regions based on their focus status, ensuring accurate measurement for low depth of field objects while maintaining overall system performance.
Solution Approach 2:
The patent segments the video frames into focused areas and defocused areas using focus detection algorithms. By dividing the image into these distinct regions, the system can apply appropriate blur calculation methods to each region, improving overall accuracy particularly for objects with low depth of field.
2Measurement precision
If traditional blur calculation methods are used, then the process is simple, but the perceived quality and duration of blur artifact are not calculated
Solution Approach 1:
The patent performs preliminary focus detection and edge identification before calculating blur artifact. By preparing the image data in advance through focus mapping and edge detection, the system enables comprehensive blur analysis including perceived quality and duration measurements without requiring overly complex real-time calculations.
Solution Approach 2:
The patent extends the traditional 2D spatial blur measurement to include temporal dimension by calculating blur duration across multiple frames. It also adds a perceptual dimension by comparing calculated blur against human vision characteristics, transforming the problem from simple pixel analysis to multi-dimensional assessment.
3Measurement precision
If full reference method is used to calculate blur artifact, then the calculation can be performed, but the computational load is high and focused area identification is not achieved
Solution Approach 1:
The patent extracts only the essential information needed for blur calculation - specifically focus status and edge characteristics - rather than processing the entire image data. By taking out only the critical features through focus detection and edge identification, the system achieves accurate blur measurement with reduced computational complexity.
Solution Approach 2:
The patent applies partial action by calculating blur artifact only in regions where focus detection indicates defocused areas, rather than processing the entire image uniformly. This selective approach reduces computational load while maintaining accuracy for the most relevant regions.
4Productivity
If no-reference method is used to calculate blur artifact, then the process is faster, but the blur artifact calculation is inaccurate for objects not in focused area
Solution Approach 1:
The patent performs preliminary focus detection to identify which regions require blur calculation. By pre-determining the focused and defocused areas, the system can then apply blur calculation only where needed, maintaining speed advantages of no-reference methods while achieving the accuracy of reference-based methods for critical regions.
Data Source
AI summary
A method and system for calculating a blur artifact in a video are disclosed. The video includes a series of frames captured at a predefined interval of time. The frames include one or more pixels. Calculating the blur artifact in the video includes identifying a focused area in a set of frames. Further, edges are detected in each of the frames. Furthermore, the blur artifact is calculated as a ratio of number of blurred pixels and total number edge pixels.