Non-Local Means Luma Chroma Separation
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Solution Overview
Problem
Conventional video communication systems face limitations in effectively separating luma (Y) and chroma (CbCr) components from composite video signals, particularly in handling modulated chroma signals and baseband luma components, which affects video decoding and rendering processes.
Innovation Solution
The use of non-local means (NLM) and weighted least square methods to determine weighting factors for separating luma and chroma components from composite video signals, allowing for precise extraction of Y, Cb, and Cr components by comparing current pixels with reference pixels across fields or frames, and solving equations based on known signal information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional video communication systems use traditional separation methods for luma and chroma components, then the system complexity remains low, but the separation precision and video quality deteriorate
Solution Approach 1:
The patent replaces conventional mechanical/filter-based separation methods with a computational approach using Non-Local Means (NLM) algorithm. This substitutes traditional signal processing mechanisms with a pixel-based computational method that analyzes relationships between pixels across different color spaces to achieve superior separation precision while maintaining manageable system complexity through software implementation
Solution Approach 2:
The patent transforms the separation problem by changing parameters from frequency-domain filtering to spatial-domain pixel relationship analysis. By converting the problem into a parameter estimation task using NLM, the system achieves better separation precision through mathematical optimization rather than relying on complex hardware filtering circuits
2Reliability
If conventional systems use simple filtering for component separation, then the processing speed is high, but the noise artifacts increase and video quality decreases
Solution Approach 1:
The patent substitutes traditional analog filtering mechanisms with a digital Non-Local Means algorithm that processes pixel data computationally. This replacement reduces noise artifacts by analyzing pixel relationships across the image rather than relying on frequency filtering, thereby improving video quality while maintaining processing efficiency through optimized computational algorithms
Solution Approach 2:
The NLM algorithm uses copying of pixel information from reference pixels to current pixels based on calculated weights. This copying mechanism allows the system to reconstruct clean signal components by aggregating information from multiple reference pixels, effectively reducing noise while preserving image quality and maintaining efficient processing
3Manufacturing precision
If conventional systems separate luma and chroma components using traditional methods, then the implementation is simple, but the decoding accuracy and rendering performance are limited
Solution Approach 1:
The patent replaces traditional hardware-based filtering implementations with a software-based Non-Local Means algorithm. This substitution enables higher decoding accuracy through sophisticated computational pixel analysis while maintaining ease of implementation through standard processing pipelines, as the algorithm can be efficiently executed using conventional digital signal processing hardware or software frameworks
Data Source
AI summary
One or more processors and/or circuits receive a composite video signal and determine a current pixel and a plurality of reference pixels. A plurality of weighting factors corresponding to the reference pixels are determined utilizing non-local means. Chroma components and/or luma components for the current pixel are determined based on weighted least squares utilizing the reference pixels, the weighting factors and information known about the composite signal, for example, sub carrier information. The composite video signal may comprise baseband Y and modulated Cb and/or Cr components. Weighting factors are determined by comparing a block of pixels about the current pixel with a block of pixels about the corresponding reference pixels in a current, previous or future frame. A set of equations comprising reference pixel data, a set of weighting factors and/or the known information may be solved to the determine signal components.