Indicia Reading Terminal Frame Processing
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Solution Overview
Problem
Indicia reading terminals face performance and cost challenges due to increased pixel density in image sensor arrays, leading to lower signal-to-noise ratio and higher memory bandwidth overhead, which affects decoding performance and hand motion tolerance.
Innovation Solution
The implementation of a variable focus imaging lens and processing techniques such as binning and windowing, where the lens is set to short range focus during binned frame exposure and long range focus during windowed frame exposure, to optimize image capture and reduce memory bandwidth while maintaining decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of pixels in the image sensor array is increased, then the resolution is improved, but the signal-to-noise ratio deteriorates and memory bandwidth overhead increases
Solution Approach 1:
The patent divides the image sensor array into multiple pixel groups (e.g., 2x2 blocks) and processes them as binned frames. Each pixel group is aggregated to produce a single output pixel, effectively segmenting the high-resolution input into lower-resolution output frames that have improved signal-to-noise ratios. This segmentation approach allows the system to process multiple frames of different resolutions simultaneously.
Solution Approach 2:
The system dynamically changes the binning parameter (aggregation level of pixel groups) based on the required output resolution and signal-to-noise ratio. By adjusting the binning factor, the system can transform the same input frame into different output resolutions, optimizing the balance between resolution and signal-to-noise ratio for different decoding requirements.
2Measurement precision
If the number of pixels in the image sensor array is increased, then the resolution is improved, but the memory bandwidth overhead increases
Solution Approach 1:
By segmenting the pixel array into bins and processing only the necessary portions of each bin, the system reduces the total number of pixels that need to be stored and transmitted. The windowing operation further segments the binned frames to process only relevant regions, significantly reducing memory bandwidth requirements compared to processing full high-resolution frames.
Solution Approach 2:
The system processes only the necessary portion of each binned frame through windowing operations, rather than processing the entire frame. This partial action approach reduces memory bandwidth overhead by avoiding the transmission and processing of unnecessary pixel data, while still maintaining sufficient resolution for the decoding task.
3Reliability
If binning is applied to the image data, then the signal-to-noise ratio is improved, but the resolution deteriorates
Solution Approach 1:
The patent applies binning to segment pixel groups into larger aggregates, which improves signal-to-noise ratio by reducing noise in each binned pixel. The system then applies windowing to select specific regions from these binned frames, allowing the system to achieve both improved signal-to-noise ratio and sufficient resolution for decoding by strategically choosing which binned regions to process.
Solution Approach 2:
The system dynamically adjusts the binning level and windowing parameters based on the specific decoding requirements and image characteristics. By making these parameters variable rather than fixed, the system can optimize the balance between signal-to-noise ratio improvement and resolution maintenance for different operational scenarios.
4Quantity of substance
If windowing is applied to the image data, then the memory bandwidth is reduced, but the field of view is limited
Solution Approach 1:
The patent combines binning and windowing operations to segment the image data into manageable portions. The windowing operation selectively processes only the necessary regions from binned frames, significantly reducing memory bandwidth requirements. The system compensates for the field of view limitation by processing multiple windowed frames at different positions and times to reconstruct the complete scene information needed for decoding.
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 enhances the signal-to-noise ratio, reduces memory overhead, and improves decoding success rates, especially in environments with lower illumination and at varying distances, thereby addressing the limitations of high pixel density image sensor arrays.
Implementation Method 1
an imaging lens for use in focusing an image of a target decodable indicia onto said two dimensional image sensor array
Implementation Method 2
a variable focus imaging lens capable of defining a plurality of best focus distances
Implementation Method 3
an image sensor integrated circuit having a two dimensional image sensor array, said two dimensional image sensor array including a plurality of pixels
Data Source
AI summary
There is described an indicia reading terminal that can be operative to process a frame of image data for attempting to decode a decodable indicia. A frame can be a frame that is among a succession of frames for subjecting to processing subsequent to and during a time a trigger signal is active. Such a succession of frames can include zero or more binned frames, zero or more unbinned frames, zero or more windowed frames, and zero or more unwindowed full frames. An indicia reading terminal can also include a variable focus imaging lens.