Partial Dynamic False Contour Detection Using Lookup Tables
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Solution Overview
Problem
Dynamic false contour (DFC) noise degrades the performance of plasma display panels (PDPs) and organic light emitting displays (OLEDs) by causing image distortion, which existing technologies have not effectively addressed.
Innovation Solution
A method and apparatus for detecting and correcting DFC noise by using lookup tables to compare pixel data values with candidate values, determining the presence of noise, and applying dithering to compensate for distortions, involving a system with line memories, comparators, and result integration modules to identify and correct DFC noise in image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lookup tables are used to detect DFC noise by comparing pixel data values, then detection accuracy is improved, but device complexity increases due to additional comparators and memory structures
Solution Approach 1:
The detection process is segmented into distinct functional blocks: line memories for storing pixel data, comparators for comparing data values against lookup tables, OR logic for combining comparison results, and buffer memories for temporal storage. This segmentation allows each component to perform a specific function efficiently, improving detection accuracy while making the complexity manageable through modular design.
Solution Approach 2:
Lookup tables containing DFC candidate values are pre-calculated and stored before the detection process begins. This preliminary action allows the comparators to quickly identify potential DFC noise by simple comparison operations during runtime, significantly improving detection accuracy without requiring complex real-time calculations that would increase device complexity.
2Manufacturing precision
If dithering is applied to compensate for data value displacement during correction, then image quality is improved, but processing time increases
Solution Approach 1:
Dithering modifies the temporal parameters of pixel data by introducing controlled random variations in the timing or sequence of pixel value updates. This parameter change allows the system to compensate for quantization errors and data value displacement, improving image quality while the random nature of dithering prevents systematic delays, thus minimizing processing time impact.
Data Source
AI summary
A method for detecting noise includes determining whether a data value of a candidate pixel in a predetermined region of an image matches a first dynamic false contour (DFC) candidate value, determining whether a data value of at least one pixel adjacent to the candidate pixel matches a second DFC candidate value, and changing the data value of the candidate pixel the prior two determinations. The data value of the candidate pixel may be changed to a value in a lookup table. The first and second DFC candidate values may also be stored in one or more lookup tables.