SIMD-Based Image Decoding for Encoded Markings
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Solution Overview
Problem
Current systems for decoding encoded data markings, such as indicia and digital watermarks, from captured images are processing-intensive and often require additional specialized circuitry, which increases complexity and power consumption, and lack flexibility in handling multiple combinations of markings.
Innovation Solution
A decoding system utilizing multiple single-instruction multiple-data (SIMD) components within a processor to perform transforms on image data, generating metadata for analysis to identify regions of interest and decode encoded data markings without the need for external specialized circuitry, allowing for efficient processing of differing combinations of indicia and digital watermarks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If additional specialized circuitry (ASIC or FPGA) is added to offload processing operations from the processor, then processing efficiency for identifying encoded data markings is improved, but device complexity and power consumption increase
Solution Approach 1:
The patent applies universality by enabling a single processor to perform multiple functions: it acts as both the general-purpose control unit and the specialized processing unit for identifying encoded data markings. The processor dynamically allocates its own internal resources (cores, SIMD units, GPUs) to handle different processing tasks, eliminating the need for separate dedicated circuitry while maintaining high processing efficiency.
Solution Approach 2:
The processor serves itself by utilizing its own internal components to perform the specialized processing tasks that would otherwise require external circuitry. The processor identifies encoded data markings, performs transforms, and generates metadata using its own computational resources, thereby avoiding the need for additional specialized hardware and reducing overall device complexity.
2Productivity
If additional specialized circuitry (ASIC or FPGA) is added to offload processing operations from the processor, then processing efficiency for identifying encoded data markings is improved, but power consumption increases
Solution Approach 1:
The processor performs multiple functions including general control and specialized marking identification using its own internal resources. By consolidating these functions into a single multi-functional unit, the system avoids the power overhead of maintaining separate dedicated circuitry, thereby improving processing efficiency without proportionally increasing power consumption.
Solution Approach 2:
The patent merges the specialized processing functions for encoded data marking identification with the general-purpose processor functions. The processor combines its cores, SIMD units, and GPUs into a unified processing architecture that handles both control tasks and specialized image analysis, reducing the total power consumption compared to having separate dedicated hardware components.
3Adaptability or versatility
If additional specialized circuitry is added to handle multiple combinations of indicia and digital watermarks, then flexibility in decoding different markings is improved, but device complexity increases
Solution Approach 1:
The patent applies dynamics by making the processor configuration flexible and adaptable rather than fixed. The processor dynamically allocates its internal resources (cores, SIMD units, GPUs) based on the specific processing requirements of different encoded data markings. This dynamic resource allocation enables the system to handle various combinations of indicia and digital watermarks without requiring physical reconfiguration or additional specialized circuitry for each marking type.
Solution Approach 2:
The processor serves as a universal processing unit capable of handling multiple types of encoded data markings through software-controlled resource allocation. By using a single multi-functional processor instead of dedicated circuitry for each marking type, the system achieves high flexibility in decoding different combinations of indicia and digital watermarks while maintaining low device complexity.
4Device complexity
If a processor uses its own internal resources to perform transforms and identify encoded data markings, then device complexity is reduced, but processing efficiency may be insufficient for high-volume decoding
Solution Approach 1:
The patent segments the processor into distinct functional units that can operate independently and in parallel: general-purpose cores for control logic, SIMD units for batch processing of image data, and GPUs for parallel transform operations. This segmentation allows each unit to be optimized for its specific task while working together as an integrated system, achieving high processing efficiency without requiring external specialized circuitry.
Solution Approach 2:
The patent transitions from sequential processing to parallel processing by utilizing multiple processing dimensions within the processor. The SIMD units process multiple data elements simultaneously, and the GPUs provide massive parallel processing capability for image transforms. This dimensional shift from single-threaded to multi-threaded parallel execution enables the processor to handle high-volume decoding workloads efficiently using only its internal resources.
Data Source
AI summary
A decoding device includes storage to store image data including grayscale values of pixels in multiple captured images, and a processor including multiple SIMD components and at least one component. For each captured image at least one available SIMD component performs: at least one transform with the grayscale values of at least one portion of the captured image to generate at least one corresponding metadata; and the at least one transform in preparation for an analysis of the at least one metadata by the at least one core component. The at least one core component is to perform: the analysis to identify at least one ROI within the captured image indicated by the at least one metadata to include at least one encoded data marking; and a decoding of the at least one ROI to attempt a decode of the at least one encoded data marking.