Optical Pattern Decoding on High-Performance Cores
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Solution Overview
Problem
Mobile devices struggle to efficiently decode optical patterns in real scenes due to issues like out-of-focus, underexposed, or small patterns, which are exacerbated by standard features like auto-focus, auto-exposure, and multi-core processing latency.
Innovation Solution
Implementing techniques such as disabling auto-focus and auto-exposure, using manual or automatic focus and exposure control, zooming, and multi-threading to optimize image acquisition and processing, allowing for improved detection and decoding of optical patterns in real scenes by adjusting camera settings based on pattern analysis and user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multi-core processing is used to improve decoding speed, then processing throughput increases, but latency increases due to context switching and thread management overhead
Solution Approach 1:
The patent segments the optical pattern recognition process into distinct functional modules (image acquisition, preprocessing, pattern detection, decoding) that can be independently optimized and executed. This segmentation allows critical time-sensitive operations to be isolated from non-critical operations, reducing overall latency while maintaining throughput.
Solution Approach 2:
The patent dynamically adjusts processing parameters such as thread priority, buffer sizes, and processing granularity based on scene complexity and device performance characteristics. This allows the system to optimize the balance between multi-core throughput and latency for different operational conditions.
2Reliability
If standard auto-focus and auto-exposure features are enabled, then image quality is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary analysis of the scene to determine whether auto-focus and auto-exposure adjustments are actually needed before executing these computationally intensive operations. By pre-assessing scene characteristics (such as detecting high-contrast patterns that don't require adjustment), the system avoids unnecessary processing steps.
Solution Approach 2:
The patent applies auto-focus and auto-exposure adjustments selectively only to regions of the image containing optical patterns, rather than processing the entire image. This partial action approach maintains image quality where needed while significantly reducing overall processing time and computational resource consumption.
3Measurement precision
If the camera processes the entire scene at high resolution, then detection accuracy improves, but power consumption and processing load increase
Solution Approach 1:
The patent applies different processing qualities to different regions of the image based on their importance. Optical pattern regions are processed at full resolution with high accuracy, while background regions use lower resolution and simplified processing. This local quality differentiation maintains detection accuracy for patterns while dramatically reducing overall power consumption and processing load.
Solution Approach 2:
The patent segments the image processing task into pattern-containing regions and non-pattern regions, applying high-resolution processing only to the former. This segmentation strategy enables the system to achieve high detection accuracy where needed while conserving energy in regions that don't require intensive processing.
Data Source
AI summary
An image of a scene is acquired by a camera of a mobile device. Hardware information is used to identify a higher-performance core and lower-performance core of a multi-core processor. An application for optical pattern scanning, such as barcode scanning, is restricted to run on the higher-performance core, for decoding an optical pattern in the image of the scene.