Image Processing Apparatus Tag-Based Implementation Counting
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Solution Overview
Problem
Existing image processing systems do not effectively track or manage the number of times specific processing operations are applied to images based on tag information, leading to inefficiencies in processing optimization and user preference implementation.
Innovation Solution
An image processing apparatus and method that utilize a tag specifying information acquisition unit to read processing content from an image processing table, apply the corresponding processing to the image, and increment a count for the number of implementations, allowing for the tracking and optimization of processing operations based on tag information, with additional features for user input and processing content management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image processing is executed based on tag information without tracking implementation counts, then the system remains simple and fast, but the ability to optimize processing based on user preferences and frequency is lost
Solution Approach 1:
The system performs preliminary actions by pre-defining multiple processing contents (first, second, third processing) with associated tag information in a table structure before actual image processing occurs. This allows the system to quickly retrieve and apply appropriate processing based on tag matches without complex real-time decision-making, thereby improving productivity while maintaining manageable system complexity through structured preparation.
Solution Approach 2:
The system implements feedback by counting the number of times each processing content is applied to images and using this count information to determine selection priority among multiple matching processing contents. The implementation count serves as feedback that influences future processing decisions, allowing the system to optimize based on actual usage patterns while maintaining a relatively simple structure by only tracking counts rather than implementing complex optimization algorithms.
2Measurement precision
If multiple processing contents are defined with tag information in a table, then processing selection becomes more accurate, but the device complexity increases due to additional data structures and matching logic
Solution Approach 1:
The system segments processing content into distinct, independently definable units (first processing content, second processing content, third processing content), each with its own tag information and implementation count. This segmentation allows for precise matching by comparing query tags against specific processing content tags in a structured table, improving selection accuracy while keeping each segment simple and manageable, thereby controlling overall system complexity.
Solution Approach 2:
The system uses parameter changes by varying the implementation count parameter for different processing contents based on their usage frequency. This parameter (implementation count) becomes a key factor in selecting which processing content to apply when multiple options match the tag information. By changing and utilizing this parameter, the system achieves more accurate and context-appropriate processing selection without requiring complex decision logic, maintaining data structure simplicity.
3Productivity
If the system tracks the number of implementations for each processing content, then processing optimization is improved, but the data management complexity increases
Solution Approach 1:
The system implements self-service by automatically incrementing the implementation count for each processing content whenever it is applied to an image. This automatic tracking occurs as part of the normal processing flow without requiring separate manual intervention or complex external management systems. The data structure updates itself through the processing operation, thereby improving processing optimization through automatic tracking while minimizing data management complexity by making the system self-updating.
Data Source
AI summary
Provided are an image processing apparatus, as well as an image processing method and recording medium storing image processing program, in which it is possible to ascertain the number of times a particular kind of processing has been implemented with regard to an image. To achieve this, an image is analyzed and tag information possessed by the image is acquired. Processing content corresponding to the acquired image tag information is read from an image processing table. Processing defined by the read processing content is implemented with regard to the image and the number of implementations of this processing is increased. The processed image is displayed.