False Contour Removal Using Gradient and Simplicity Metrics
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Solution Overview
Problem
Existing methods for removing false contours in digital videos, such as those caused by bit depth conversion or compression, often result in artificial patterns or blur real edges, as they lack precision in identifying and isolating the contours for effective removal.
Innovation Solution
A method that detects false contours by calculating pixel gradients and simplicity, determining contour inclusion based on these metrics, and selectively removes them using adaptive smoothing filters, specifically designed for small-sized displays like DMB players and mobile phones with limited memory and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If dithering method is used to increase bit depth, then false contour is removed, but artificial patterns occur in entire video
Solution Approach 1:
The video processing is segmented into distinct stages: false contour detection using gradient and simplicity metrics, contour identification, and selective smoothing. This segmentation allows the algorithm to target only false contours without affecting other video regions, preventing artificial pattern generation while removing false contours
Solution Approach 2:
The smoothing operation is applied locally only to detected false contour regions rather than globally across the entire video. By calculating gradient and simplicity metrics for each pixel and applying smoothing selectively based on these local characteristics, the method removes false contours while preserving detail areas and avoiding artificial patterns in non-contour regions
2Object-affected harmful factors
If low pass filter is used to remove false contour, then false contour is removed, but real edges are blurred
Solution Approach 1:
The algorithm performs preliminary detection of false contours using gradient and simplicity metrics before applying any smoothing operation. By identifying and marking false contour regions in advance, the smoothing filter can be selectively applied only to these regions, preventing blurring of real edges that were detected and excluded from smoothing
Solution Approach 2:
Different processing treatments are applied to different regions: false contour regions undergo smoothing while detail areas and real edges preserve their original characteristics. The gradient and simplicity metrics enable this local differentiation, ensuring that smoothing is applied only where false contours exist without affecting the sharpness of real edges
3Device complexity
If conventional methods are used for false contour removal, then processing is simple, but detection precision is insufficient
Solution Approach 1:
The method introduces two key parameters for detection: gradient (measuring intensity change between adjacent pixels) and simplicity (measuring uniformity in local regions). By changing from conventional single-metric detection to this dual-parameter approach, the system achieves superior detection precision while maintaining computational efficiency through straightforward arithmetic operations
Data Source
AI summary
A method and an apparatus for detecting and removing a false contour, a method and an apparatus for verifying whether a pixel is included in a contour, and a method and an apparatus for calculating simplicity are provided. The method for detecting and removing the false contour includes: verifying whether a pixel of an input video is included in a contour; calculating simplicity of the pixel; determining whether the pixel is included in a false contour based on the simplicity and based on whether the pixel is included in the contour; and removing the false contour from the input video via smoothing with respect to the false contour.