ISP Auto-Focus Statistics Collection Engine
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Solution Overview
Problem
Conventional digital imaging devices face challenges in addressing errors and distortions such as defective pixels, non-uniform light intensity, and noise amplification during image processing, particularly due to manufacturing defects and lens imperfections, which can result in artifacts and undesirable noise in images.
Innovation Solution
Implementing a statistics collection engine in the image signal processor to collect data on auto-white balance, auto-exposure, and auto-focus, and using pixel filters to conditionally accumulate data based on YC1C2 characteristics, allowing for improved color matching and lens shading correction, as well as demosaicing techniques that account for edge locations and directions within the image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image processing techniques are applied to correct defective pixels and non-uniform light intensity, then image quality may be improved, but manufacturing defects and lens imperfections cannot be fully addressed due to lack of accurate statistics
Solution Approach 1:
The patent applies preliminary action by collecting image statistics (mean, standard deviation, skewness, kurtosis) before performing image processing operations. The statistics collection engine gathers data from multiple image blocks and computes statistical parameters in advance, which are then used to guide defective pixel correction and non-uniform light intensity compensation algorithms, enabling more accurate corrections based on actual image content characteristics
Solution Approach 2:
The patent implements feedback by using collected image statistics to continuously refine image processing parameters. The system computes statistical measures from image blocks, feeds this information back to correction algorithms, and adjusts processing gains and offsets dynamically based on the statistical feedback, creating a closed-loop system that adapts to varying image conditions
2Manufacturing precision
If conventional sharpening techniques are applied to enhance image detail, then image clarity may be improved, but noise in the image signal is amplified and noise cannot be distinguished from edges and textured areas
Solution Approach 1:
The patent applies local quality by computing separate statistical parameters (mean, standard deviation, skewness, kurtosis) for different image blocks and using these local statistics to guide sharpening operations. The system identifies edges and textured areas through local statistical analysis and applies differentiated sharpening strength to different regions, preserving detail in edges while suppressing noise amplification in uniform areas
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting sharpening parameters based on collected image statistics. The system modifies sharpening gain and threshold parameters according to the statistical characteristics (standard deviation, skewness, kurtosis) of different image regions, enabling adaptive sharpening that responds to local image content rather than applying uniform sharpening across the entire image
3Manufacturing precision
If conventional demosaicing techniques are applied to interpolate color data, then full color image reproduction may be achieved, but edge artifacts such as aliasing, checkerboard artifacts, and rainbow artifacts are introduced
Solution Approach 1:
The patent applies preliminary action by collecting and analyzing image statistics (mean, standard deviation, skewness, kurtosis) from multiple image blocks before performing demosaicing operations. The system uses these pre-computed statistics to identify edge locations and directions, and to determine appropriate interpolation parameters, enabling the demosaicing algorithm to adapt to local image characteristics and avoid edge artifacts
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting demosaicing parameters based on collected image statistics. The system modifies interpolation weights, edge detection thresholds, and filtering parameters according to the statistical characteristics of different image regions, enabling adaptive demosaicing that preserves edge sharpness while minimizing artifacts like aliasing, checkerboard patterns, and rainbow effects
4Measurement precision
If a statistics collection engine is implemented to collect accurate image statistics, then image processing accuracy is improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the image into multiple blocks and collecting statistics independently for each block. The statistics collection engine processes image data in segmented regions, computing statistical parameters (mean, standard deviation, skewness, kurtosis) for each block separately. This segmentation approach enables parallel processing, reduces computational complexity compared to analyzing the entire image at once, and allows localized statistical analysis that is more representative of local image characteristics
Solution Approach 2:
The patent implements universality by designing a statistics collection engine that serves multiple image processing functions simultaneously. The same collected statistics (mean, standard deviation, skewness, kurtosis) are reused across different processing stages including defective pixel correction, non-uniform light intensity compensation, sharpening, and demosaicing. This multi-functional approach eliminates the need for separate statistics collection for each processing operation, reducing overall system complexity
Data Source
AI summary
Various techniques are disclosed for collecting and processing auto-focus statistics data in an image signal processor (ISP). In one embodiment, a statistics collection engine in an ISP front-end processing unit may be configured to collect coarse (based on decimated raw data) and fine auto-focus statistics. Coarse auto-focus statistics may be collected on decimated Bayer RGB data and/or on linear camera luma values. Fine auto-focus statistics may be collected on raw Bayer RGB using a combination of a horizontal filter and edge detector, or may be collected on BayerY data (by applying a 3×1 transform to the raw Bayer RGB data). Edge sums may be accumulated using the filter outputs to determine auto-focus statistics.