Border Region Processing in Image Frame Rate Up-Conversion
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Solution Overview
Problem
Traditional frame rate up-conversion algorithms fail to effectively handle border regions in images, leading to artifacts where black lines can move into the picture and disrupt the background motion, causing unwanted border lines to appear in interpolated or extrapolated images.
Innovation Solution
A method is introduced to identify border regions in images by calculating average and difference values of luminance properties across rows and columns, using a classification parameter to distinguish border regions from internal regions, and excluding border elements from interpolation or extrapolation to prevent these artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional frame rate up-conversion algorithms are used without special border treatment, then the processing is simple and fast, but border lines move into the picture and create artifacts in the interpolated image
Solution Approach 1:
The image is divided into border regions and internal regions based on luminance threshold detection. Border regions are identified by comparing luminance values against thresholds and are handled separately from internal regions, preventing border lines from moving into the picture while maintaining simple processing
Solution Approach 2:
Different processing methods are applied to different regions: border regions use one processing approach (preserving border lines) while internal regions use another (motion estimation). This local differentiation resolves the contradiction by applying complexity only where needed
2Reliability
If motion estimation is applied to all regions including borders, then the processing is uniform and simple, but border lines are incorrectly processed and move into the picture
Solution Approach 1:
Before applying motion estimation, the algorithm preliminarily identifies border regions by detecting luminance thresholds. This preliminary classification allows border lines to be protected from incorrect motion estimation processing, maintaining their stability while keeping the overall process simple
3Productivity
If no special border handling is implemented, then the algorithm remains simple and fast, but artifacts appear where border lines enter the internal region
Solution Approach 1:
Border regions are extracted and separated from the main processing flow. By taking out border regions and handling them separately, the algorithm prevents harmful artifacts while maintaining high processing speed, as the extraction uses simple luminance threshold comparisons
Data Source
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AI summary
A border region (70) is identified in an image (40) by calculating an average of pixel values in a row (20) or column (10) of the image (40). Differences in property values are determined between each pixel (30) in the row (20) or column (10) and a neighboring pixel (30) present on a same column (10) or row (20) but in a neighboring row (20) or column (10). An average difference is calculated based on these differences. The pixels (30) in the row (20) or column (10) are classified as belonging to a border region (70) or internal region (75) of the image (40) based on the average pixel value and the average pixel difference.