Pixel Interpolation Using Cross-Correlation Edge Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional video processing techniques for deinterlacing and upscaling often fail to accurately estimate pixel values due to inadequate consideration of edge direction, leading to significant interpolation errors and visual artifacts, and require substantial processing effort.
Innovation Solution
A video processing device that employs edge-dependent pixel interpolation using cross-correlation to detect and verify the validity of edges, allowing for diagonal interpolation based on edge angle or alternative methods like vertical interpolation when edges are invalid.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional interpolation techniques are used to estimate pixel values, then processing effort is reduced, but interpolation accuracy deteriorates due to failure to account for edge direction
Solution Approach 1:
The patent changes the parameter of edge detection by introducing cross-correlation analysis between vertically offset lines to detect edge angles. This allows the system to adapt interpolation parameters (horizontal vs. vertical) based on detected edge orientation, improving accuracy without requiring complex multi-field analysis.
Solution Approach 2:
The patent applies different interpolation methods locally based on edge characteristics. When edges are detected at specific angles (particularly near horizontal), diagonal or horizontal interpolation is used. When no valid edges are detected, vertical interpolation is applied. This local adaptation improves overall accuracy without uniformly increasing complexity.
2Measurement precision
If edge-dependent interpolation is used to improve pixel estimation accuracy, then interpolation errors are reduced, but processing effort increases due to analysis over many successive fields
Solution Approach 1:
The patent performs preliminary edge detection using cross-correlation on the current field data before interpolation. By detecting edges and their angles in advance within the same field, the system avoids the need for time-consuming analysis across multiple successive fields, reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent extracts only the necessary edge information (presence and angle) from the video data using cross-correlation, rather than performing comprehensive multi-field analysis. This extraction of key parameters enables accurate interpolation with reduced processing time by focusing only on the most relevant features.
3Reliability
If conventional edge detection is used to identify edges for interpolation, then edge detection is simplified, but reliability deteriorates due to false edge detection and lack of validity evaluation
Solution Approach 1:
The patent implements feedback by using cross-correlation results to verify edge validity. The cross-correlation process provides a reliability metric that feedback into the interpolation decision, allowing the system to confirm whether detected edges are valid before using them, thereby improving reliability without excessive complexity.
4Device complexity
If interpolation without edge direction consideration is used, then processing complexity is reduced, but visual artifacts increase due to significant interpolation errors
Solution Approach 1:
The patent introduces dynamics by making the interpolation method adaptive based on detected edge directions. The system dynamically selects between vertical, horizontal, or diagonal interpolation based on real-time edge analysis, allowing it to minimize visual artifacts in edge regions while maintaining simplicity in non-edge regions.
Data Source
AI summary
A pixel interpolation process is based on detection of a potential edge in proximity to a pixel being estimated, and the angle thereof. The potential edge and its angle is determined based on filtering of offset or overlapping sets of lines from a pixel window centered around the pixel being estimated and then cross-correlating the filter results. The highest value in the correlation result values represents a potential edge in proximity to the pixel being estimated and the index of the highest value represents the angle of the potential edge. This information is used in conjunction with other information from the cross-correlation and analysis of the differences between pixels in proximity to verify the validity of the potential edge. If determined to be valid, a diagonal interpolation based on the edge and its angle is used to estimate the pixel value of the pixel. Otherwise, an alternate interpolation process, such as vertical interpolation, is used to estimate the pixel value for the pixel.