Image Interpolation Using Dynamic Searched Ranges
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Solution Overview
Problem
Conventional edge-adaptive interpolation methods face challenges in defining an appropriate searched range, leading to image quality degradation as the same range that improves quality for one image may degrade the quality of another.
Innovation Solution
An image interpolation method that calculates brightness changes between adjacent pixels, determines regions with similar brightness changes, and selects groups of pixels with the highest similarity symmetrically positioned with respect to the interpolated pixel to determine pixel values, ensuring high image quality during longitudinal scaling and interlaced-to-progressive format conversion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed searched range is used for edge-adaptive interpolation, then the processing is simple and fast, but image quality degrades because the same range cannot be appropriate for all images
Solution Approach 1:
The patent applies dynamics by making the searched range variable rather than fixed. The range is dynamically adjusted based on the gradient direction at each interpolated pixel position, allowing the system to adapt to different image structures (horizontal, vertical, or oblique lines) and achieve high quality across various image types without sacrificing processing efficiency
Solution Approach 2:
The patent implements local quality by setting different searched ranges for different regions of the image based on local gradient characteristics. Each interpolated pixel has its own optimized searched range determined by the gradient direction in its neighborhood, allowing the system to tailor the interpolation search to local image features rather than applying a uniform range throughout
2Manufacturing precision
If the searched range is expanded to improve image quality for all cases, then more pixels are available for interpolation, but processing complexity and time increase
Solution Approach 1:
The patent changes the parameter of searched range size based on gradient direction. Instead of using a consistently large range that increases complexity, the system adjusts the range parameter dynamically - using smaller ranges for horizontal and vertical lines and larger ranges for oblique lines, thereby optimizing the balance between quality and complexity for each local region
3Device complexity
If a small searched range is used to maintain simple processing, then processing is fast, but image quality degrades for oblique lines and complex structures
Solution Approach 1:
The system dynamically adjusts the searched range based on gradient direction detection. For horizontal and vertical lines where small ranges are sufficient, the processing remains simple and fast. For oblique lines and complex structures where larger ranges are needed, the system automatically expands the search range to capture the necessary pixel information, thereby adapting the complexity to the actual image content
Data Source
AI summary
An image interpolation method obtains an image line pixel value between two adjacent image lines. The method acquires a luminance change of pixels of two image lines and decides a region of two image lines where only one portion similar to a part of the luminance change of one of the image lines exists in the luminance change of the other image line in the vicinity of the image to be interpolated. Then, among pixel sets located at the symmetric positions about the object image to be interpolated with respect to the pixel within the region, the set having the highest similarity is selected. By using the pixel value of the selected set, the pixel value of the pixel to be interpolated is decided.