Image Sensor Binning Areas Reduce Data Size
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Solution Overview
Problem
The increasing size of image data generated by image sensors poses challenges in maintaining a high frame rate and reducing power consumption, particularly in high-definition and high-quality image capture applications.
Innovation Solution
The image sensor employs a pixel array with an RGBW or RGBY pattern, where pixels share a floating diffusion node and a readout circuit that processes pixel signals from color and white pixels within binning areas during a single frame period, allowing for efficient generation and merging of image data to reduce data size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image sensors capture high-definition and high-quality images, then image quality is improved, but image data size increases
Solution Approach 1:
The pixel array is divided into multiple binning areas, where pixels within each area share a floating diffusion node. This segmentation allows the sensor to process and merge signals from multiple pixels through a common node, effectively reducing the overall data size while preserving high-definition image quality across different spatial regions.
Solution Approach 2:
Multiple pixels within each binning area merge their signals at the shared floating diffusion node. The readout circuit then combines these merged signals to generate final pixel values, reducing the total data volume while maintaining image quality through intelligent signal integration across color and white/yellow pixels.
2Measurement precision
If image sensors increase data output for high-definition imaging, then image quality is improved, but power consumption increases
Solution Approach 1:
By dividing the pixel array into binning areas with shared floating diffusion nodes, the system reduces the number of independent signal processing paths. This segmentation decreases the overall power consumption while maintaining high-definition image quality through coordinated signal processing within each binning area.
Solution Approach 2:
The merging of signals at shared floating diffusion nodes reduces the number of separate readout operations required. By combining signals from multiple pixels through common nodes and processing them collectively, the system lowers power consumption while preserving image quality through efficient signal integration.
3Measurement precision
If image sensors increase data output for high-definition imaging, then image quality is improved, but frame rate decreases
Solution Approach 1:
The segmentation of the pixel array into binning areas with shared floating diffusion nodes enables parallel processing of multiple pixel signals through common output paths. This reduces the total number of readout operations required per frame, thereby maintaining high frame rates while preserving high-definition image quality.
Solution Approach 2:
By merging signals from multiple pixels at shared floating diffusion nodes and processing them collectively through the readout circuit, the system reduces the overall processing time per frame. This signal merging approach maintains high frame rates while delivering high-quality images through efficient data reduction.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables a high frame rate while minimizing power consumption by reading out pixel signals from color and white pixels in a single frame period, effectively managing the increased data size and improving image processing efficiency.
Implementation Method 1
An image sensor generates an image of an object by using a photoelectric conversion device that reacts according to an intensity of light reflected by the object
Data Source
AI summary
An image sensor includes: a pixel array including a plurality of pixels divided into a plurality of binning areas; a readout circuit configured to, from the plurality of binning areas, receive a plurality of pixel signals including a first sensing signal of first pixels and a second sensing signal of second pixels during a single frame period and output a first pixel value corresponding to the first pixels and a second pixel value corresponding to the second pixels based on the plurality of pixel signals; and an image signal processor configured to generate first image data based on a plurality of first pixel values corresponding to the plurality of binning areas, generate second image data based on a plurality of second pixel values corresponding to the plurality of binning areas, and generate output image data by merging the first image data with the second image data.