Automated Window Memoization Compiler for Image Processing
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Solution Overview
Problem
Current image processing technologies face challenges in achieving real-time performance due to the computational expense of algorithms, with conventional software implementations struggling to keep pace with increasing image resolution and complexity, and hardware solutions being costly and time-consuming to implement.
Innovation Solution
The development of an automated window memoization compiler and module that optimizes image processing algorithms by reducing computational redundancy, allowing for faster processing without the need for detailed profiling information, and applicable in both software and hardware platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional software implementations are used for image processing, then ease of operation is maintained, but processing speed deteriorates due to computational expense
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing results of expensive image processing operations in a lookup table (memoization table). When the same input image or image region is processed again, the pre-computed results are retrieved directly without re-executing the full algorithm, thereby significantly reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent uses copying by creating a simplified representation (copy) of the image processing results in a compact data structure (lookup table). Instead of storing the entire complex processing output, only the essential result data is copied and stored, enabling fast retrieval while reducing memory requirements.
2Speed
If specialized hardware platforms (FPGA/ASIC) are used, then processing speed is improved, but device complexity and manufacturing cost increase
Solution Approach 1:
The patent replaces the mechanical/hardware approach with a software-based memoization system. Instead of designing specialized hardware circuits to accelerate image processing, the invention uses software algorithms that automatically detect redundant computations and reuse results, achieving hardware-level performance improvements through software optimization alone.
Solution Approach 2:
The patent implements self-service by enabling the software system to automatically identify and eliminate redundant computations without external intervention. The memoization mechanism self-adjusts based on input data characteristics, automatically determining which computations can be skipped without requiring manual profiling or detailed runtime information about program behavior.
3Productivity
If generic memoization techniques are applied, then ease of operation is maintained, but performance improvement is limited due to lack of customization
Solution Approach 1:
The patent applies local quality by tailoring the memoization strategy to specific image processing algorithms and data characteristics. Instead of using a one-size-fits-all approach, the system analyzes the particular algorithm being executed and adjusts the memoization parameters, table structure, and retrieval strategies to match the specific computational patterns and data types involved.
Data Source
AI summary
The present invention provides a system, method and computer program providing automated window memoization which is an automated optimization of image processing. The method comprises inputting to a memoization compiler an image set, hardware or software code implementing one or more image processing algorithms, and one or more design parameters. The memoization compiler processes the image set for the image processing algorithms and implements hardware or software code for optimizing the image processing algorithms based on the design parameters.