Chroma Noise Reduction Using Sparse Filtering in YCC Image Signal Processors
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Solution Overview
Problem
Conventional image processing techniques fail to adequately address image distortions and errors introduced by digital imaging devices, such as defective pixels, lens imperfections, and color reproduction variations, often leading to artifacts like aliasing, checkerboard artifacts, and loss of image information, especially in high contrast images.
Innovation Solution
The implementation of an image signal processor logic that receives image data in YCC format, uses a sparse filter to reduce chroma noise in both chrominance components, with a noise threshold determined based on the YCC image data components, and performs operations using signed pixel data to preserve information and correct for defects like defective pixels and lens shading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image processing techniques are used, then processing speed is maintained, but chroma noise is not adequately reduced and image quality deteriorates
Solution Approach 1:
The image processing is divided into separate luminance and chrominance components in YCC color space, allowing independent processing of chroma noise reduction without affecting luma quality. This segmentation enables targeted noise filtering on chroma channels while preserving overall image processing efficiency.
Solution Approach 2:
The patent applies different processing strategies to different regions of the image based on local characteristics. By analyzing local variance and comparing chroma values with neighboring pixels, the filter adapts its strength locally, reducing noise in uniform regions while preserving detail in textured areas, thus achieving high quality noise reduction without uniformly degrading processing speed.
2Manufacturing precision
If aggressive noise filtering is applied to reduce chroma noise, then chroma noise reduction quality improves, but image detail and color accuracy are lost
Solution Approach 1:
The noise reduction filter dynamically adjusts its strength based on local image characteristics. By calculating local variance and comparing chroma values with neighboring pixels, the filter becomes stronger in uniform regions where noise is prominent and weaker in textured regions where image detail exists, thus preserving information while reducing noise.
Solution Approach 2:
The patent uses feedback mechanisms by comparing processed chroma values with original values and analyzing local statistical properties. This feedback allows the algorithm to detect when noise reduction is excessive and adjust accordingly, preventing loss of image detail and color accuracy while maintaining noise reduction effectiveness.
3Manufacturing precision
If conventional filtering methods are used, then processing simplicity is maintained, but chroma noise reduction effectiveness is insufficient
Solution Approach 1:
The patent transforms the problem by changing the color space parameter from RGB to YCC, which separates luminance and chrominance. This parameter change allows independent manipulation of chroma components, enabling more effective noise reduction through operations like chroma subsampling and targeted filtering, while the increased algorithmic complexity is justified by the significant improvement in noise reduction effectiveness.
4Productivity
If chroma subsampling is applied, then data processing load is reduced, but chroma noise becomes more apparent
Solution Approach 1:
The patent applies noise reduction filtering to the chroma components before or during the subsampling process. By performing preliminary noise reduction on the full-resolution chroma data and then subsampling, the harmful chroma noise is reduced before the data processing load is decreased, thus maintaining both processing efficiency and noise reduction effectiveness.
Data Source
AI summary
Systems and methods for reducing chrominance (chroma) noise in image data are provided. In one example of such a method, image data in YCC format may be received into logic of an image signal processor. Using the logic, noise may be filtered from a first chrominance component or a second chrominance component, or both, of the image data, using a sparse filter and a noise threshold. The noise threshold may be determined based at least in part on two of the components of the YCC image data.