Horizontal Cost Engine for Image Feature Identification
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Solution Overview
Problem
Current image analysis systems face computational inefficiencies when identifying features within dynamic images, particularly in high-definition video feeds, due to the need for extensive pixel comparisons and hardware resources, which increases processing time and power consumption.
Innovation Solution
The proposed image analysis system employs a structural arrangement of difference calculators and optimized hardware configurations, such as field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs), to reduce computational burden by using horizontal cost units and cascading SAD values, eliminating the need for barrel shifters and accumulator feedback paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive pixel comparisons are performed to identify features in high-definition video feeds, then measurement precision is improved, but use of energy and processing time increase
Solution Approach 1:
The patent divides the image processing task into multiple processing cycles, where each cycle handles a subset of pixel comparisons. The system segments the search area into multiple regions and processes them in parallel across different cycles, reducing the computational burden in each individual cycle while maintaining overall feature identification accuracy.
Solution Approach 2:
The system implements periodic action by performing pixel comparisons in discrete processing cycles rather than continuously. Each processing cycle completes a specific set of comparisons before transitioning to the next cycle, allowing for efficient resource management and reduced power consumption compared to continuous processing.
2Measurement precision
If extensive pixel comparisons are performed to identify features in high-definition video feeds, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent segments the large-scale pixel comparison task into multiple smaller processing cycles, each handling a specific subset of comparisons. This segmentation allows the system to make progress on feature identification in each cycle rather than requiring completion of all comparisons before any results are available, reducing overall processing time.
Solution Approach 2:
The system performs preliminary actions by preparing and organizing pixel data before the actual comparison process. Processing cycles are structured to pre-load and pre-position data in optimal configurations, reducing the time required for actual comparisons and enabling faster feature identification.
3Measurement precision
If traditional hardware configurations with barrel shifters and accumulator feedback paths are used, then measurement precision is maintained, but device complexity and use of energy increase
Solution Approach 1:
The patent extracts and removes the barrel shifter component from the traditional hardware configuration. The system achieves the necessary pixel rotation and alignment functions through alternative methods that do not require the complex barrel shifter structure, thereby reducing device complexity while maintaining calculation accuracy.
Solution Approach 2:
The system replaces the mechanical accumulator feedback path with a simplified computational approach. Instead of using physical feedback loops and accumulators, the patent employs direct computational methods that achieve the same mathematical results with fewer hardware components, reducing complexity while preserving precision.
Data Source
AI summary
Methods, systems, apparatus and articles of manufacture to identify features within an image are disclosed herein. An example apparatus includes a horizontal cost (HCOST) engine to apply a first row of pixels of a macroblock to an input of a first HCOST unit, the first HCOST unit including a number of difference calculators; and a difference calculator engine to apply corresponding rows of pixels of a search window of a source image to corresponding ones of the number of difference calculators of the first HCOST unit, the corresponding ones of the number of difference calculators to calculate respective sums of absolute difference (SAD) values between (a) the first row of pixels of the macroblock and (b) the corresponding rows of pixels of the search window.