Image Boundary Discrimination via Gradient Dispersion Analysis
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Solution Overview
Problem
Existing image processing techniques, such as virtual algorithms, face challenges in accurately discriminating image boundaries, leading to issues like color aliasing, which affects the display effect and requires constant optimization and filter adjustments for different image types and locations.
Innovation Solution
A method that processes luminance signals to identify sharp boundaries by forming n×n and (n+2)×(n+2) matrices, calculating gradients and standard deviations, and determining dispersion to classify boundary directions, allowing for the selection of appropriate filters to improve image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If universal filter is adopted for image processing, then processing simplicity is maintained, but color aliasing occurs at image boundaries
Solution Approach 1:
The patent applies different filtering strategies to different regions of the image based on boundary detection. Boundary regions use one filtering approach while non-boundary regions use another, making the filter application locally optimized rather than uniformly applied across the entire image.
Solution Approach 2:
The image is segmented into boundary regions and non-boundary regions through gradient calculation and threshold comparison. This segmentation allows selective application of different filtering methods to appropriate regions, preventing color aliasing at boundaries while maintaining processing efficiency elsewhere.
2Object-affected harmful factors
If boundary detection algorithms are implemented to reduce color aliasing, then display quality is improved, but processing complexity increases
Solution Approach 1:
The patent replaces complex boundary detection mechanisms with a simplified gradient-based approach. By calculating gradients in horizontal and vertical directions and comparing them against thresholds, the system achieves effective boundary detection without requiring sophisticated algorithms, thus reducing processing complexity while maintaining quality improvement.
Data Source
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AI summary
Disclosed are a method and a device for discriminating a boundary of image, and a display panel, for effectively discriminating whether an image has a boundary and in which direction the boundary is. The method for discriminating the boundary of image comprises: receiving an image information to be discriminated to form a matrix of grayscale parameter values, and dividing, with a grayscale parameter value corresponding to a sub-image unit to be processed as a center, the matrix of grayscale parameter values into a n×nmatrix and a (n+2)×(n+2) matrix (S101); determining respectively in the n×n matrix and the (n+2)×(n+2)matrix: a minimum gradient and a minimum standard deviation in a row direction, in a column direction, in a first diagonal direction, and in a second diagonal direction, dispersion with respect to the minimum standard deviation and dispersion with respect to the minimum gradient (S102; S103); outputting a first code value when the determined dispersion is greater than N multiples of a minimum corresponding thereto, outputting a second code value when the determined dispersion is smaller than the N multiples of the minimum corresponding thereto (S104); determining, based on an outputted code value, whether the image to be discriminated has a boundary and in which direction the boundary is (S105)..