Bisection Pattern Detection for Deinterlacing Low Angle Edges
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Solution Overview
Problem
Conventional edge direction detection techniques in video deinterlacing often misdetect low angle directions, leading to inaccuracies in interpolating missing pixels, especially in complex patterns like textures and thin lines against uniform backgrounds.
Innovation Solution
The method involves detecting the Bisection pattern within a constructed window for each missing pixel, using a binary map to count value changes and determining if the pixel is within a Bisection pattern, which refines the detected direction for improved low angle direction detection and reduces misdetection probability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional edge direction detection techniques are used, then the deinterlacing process can be performed, but low angle directions are misdetected leading to reduced accuracy
Solution Approach 1:
The detection process is segmented into two independent parts: Bisection pattern detection and conventional edge direction detection. The Bisection pattern detector identifies specific patterns (vertical edges, horizontal edges, corners) and generates a decision signal that refines the output of the conventional edge direction detector. This segmentation allows each detector to specialize in different aspects, improving overall accuracy while maintaining low misdetection probability.
Solution Approach 2:
A Bisection pattern decision signal acts as an intermediary between the conventional edge direction detection and the final direction determination. This intermediary signal provides additional contextual information about the local pattern, allowing the system to refine the detected direction and correct misdetections without requiring a complete redesign of the detection process.
2Difficulty of detecting and measuring
If the edge direction detector is made more sensitive to detect low angle directions, then detection capability improves, but misdetection probability increases in complex areas
Solution Approach 1:
The Bisection pattern detector applies different detection strategies to different local patterns. By identifying specific patterns (vertical edges, horizontal edges, corners) in local regions and generating corresponding decision signals, the system adapts the detection approach to the local characteristics, improving reliability in complex areas while maintaining sensitivity to low angle directions.
Solution Approach 2:
The Bisection pattern detection is performed in parallel with conventional edge direction detection, providing preliminary information about the local pattern before the final direction determination. This preliminary action allows the system to prepare refinement strategies in advance, correcting potential misdetections before they affect the final output.
3Productivity
If a simple directional linear interpolation is used, then the deinterlacing process is computationally efficient, but the quality of interpolated pixels deteriorates when direction detection is inaccurate
Solution Approach 1:
The Bisection pattern detection and direction refinement are performed as preliminary steps before the actual interpolation process. By preparing accurate direction information in advance, the system ensures that the simple directional linear interpolation uses correct direction parameters, thereby maintaining high interpolated pixel quality without requiring complex interpolation algorithms.
Solution Approach 2:
The system changes the direction parameter used in the interpolation process based on the Bisection pattern decision. By refining the direction parameter before interpolation, the system maintains high pixel quality using the computationally efficient directional linear interpolation method, avoiding the need for more complex interpolation algorithms.
Data Source
AI summary
A method for detecting the Bisection pattern for use in conjunction with the deinterlacing is to improve low angle direction detection capability. A method of Bisection pattern detection operates in an interlaced video containing both top and bottom fields. For each missing pixel in a current input field, a window W is constructed whose center pixel is at the considered missing pixel. A binary map is generated which includes rows of values corresponding to pixels in the window, wherein the values indicate if each element of the window is greater than the sample mean of the area surrounding the missing pixel. The number of value changes in the values in each row of the binary map is counted. It is then determined whether or not the missing pixel is within the Bisection pattern based on said counts. In order to maintain both the low angle edge direction detection capability and low misdetection probability, complicated areas are identified by the Bisection pattern detection method. Then deinterlacing is performed.