Digital Image Pixel Binning for Even Optical Center Distribution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
High pixel resolution in image sensors leads to increased image data processing demands, requiring higher speed and capacity in data processors and memory, while reducing pixel resolution for video recording results in poor image quality due to reduced light sensitivity and dynamic range, and existing pixel binning methods cause clustering of optical centers, affecting image quality.
Innovation Solution
A method for reducing digital image resolution by binning pixels into macro pixels with evenly distributed optical centers, using a color mask with specific pixel selection and rotation techniques to maintain image quality and reduce aliasing, allowing for downsizing without significantly degrading the image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel resolution is increased to improve image quality, then image quality is improved, but data processing demands and memory capacity requirements increase
Solution Approach 1:
The patent combines multiple adjacent pixels into single macro pixels through binning operations. Specifically, it merges pixels in a 2×2 pattern and also merges pixels in diagonal patterns, creating macro pixels that aggregate signal from multiple underlying pixels. This reduces the total number of pixels by a factor of four while preserving image quality through the combined signal strength of the merged pixels.
2Quantity of substance
If pixel resolution is reduced to decrease data amount, then data amount is reduced, but image quality deteriorates due to reduced light sensitivity
Solution Approach 1:
The patent merges signals from multiple adjacent pixels into single macro pixels, thereby increasing the light-sensitive area of each macro pixel. This binning approach maintains light sensitivity by aggregating photons collected across multiple pixels, while simultaneously reducing the total pixel count by four times, thus decreasing data amount without sacrificing image quality.
3Quantity of substance
If conventional pixel binning is used to reduce data amount, then data amount is reduced, but optical centers become clustered affecting image quality
Solution Approach 1:
The patent divides the pixel array into multiple overlapping binning patterns, specifically implementing both horizontal/vertical 2×2 binning and diagonal binning patterns. This segmentation approach distributes macro pixels more evenly across the image sensor, preventing the clustering of optical centers that occurs with conventional single-pattern binning, thereby maintaining better spatial distribution and image quality.
Solution Approach 2:
The patent introduces diagonal binning patterns as an additional dimension to the conventional horizontal/vertical binning approach. By merging pixels along diagonal directions in addition to orthogonal directions, the patent creates a more uniform two-dimensional distribution of macro pixels across the sensor array, eliminating the clustering problem and improving spatial coverage.
Data Source
AI summary
The invention relates to a method for reducing the pixel resolution of a digital image by binning pixels together to form macro pixels. The digital image comprises pixels arranged in a color mask of at least three different colors. The color mask can be a Bayer mask comprising one red pixel, two green pixels and one blue pixel. The pixel binning method comprises selecting a number of pixels of each color. For example, in order to obtain a factor two resolution reduction, four red pixels can be binned for generating a red macro pixel and eight green pixels can be binned for generating two green macro pixels. By selecting for example only one blue pixel and using that pixel for generating a blue macro pixel the optical centers of the macro pixels will be evenly distributed over the image.