Image Processing Device Tone Correction via Histogram Segmentation
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Solution Overview
Problem
Existing image processing methods require complex parameters and significant computational resources, making tone correction processing difficult for users without expertise, especially when applied to entire images.
Innovation Solution
An image processing device and method that displays user-friendly input parameters, converts them into more complex internal parameters, and performs tone correction processing using these internal parameters, simplifying the process for users by reducing computational burden and parameter complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tone correction processing is performed by dividing image data into multiple blocks and generating histogram data for each block, then computation amount is reduced, but parameter complexity increases and user understanding becomes difficult
Solution Approach 1:
The image data is divided into multiple blocks, and histogram data is generated for each block separately. This segmentation allows computation to be performed on smaller, manageable units rather than the entire image at once, reducing the overall computation amount while maintaining processing quality.
Solution Approach 2:
A convertor is introduced as an intermediary component that automatically transforms user-friendly first input parameters into the complex second input parameters required for tone correction processing. This intermediary handles the parameter complexity issue by performing the conversion automatically, so users don't need to understand or manually set the complex parameters.
2Manufacturing precision
If tone correction processing is performed on entire image data, then comprehensive tone enhancement is achieved, but computation amount becomes excessive
Solution Approach 1:
The image is divided into multiple blocks for separate processing. Each block undergoes histogram-based tone correction independently, ensuring comprehensive tone enhancement across the entire image while keeping computation for each block manageable and efficient.
Solution Approach 2:
Instead of processing the entire image uniformly, the system performs partial processing on individual blocks. This allows tone correction to be applied comprehensively across all blocks while maintaining computational efficiency by limiting the processing scope to smaller block units rather than the full image at once.
3Manufacturing precision
If multiple parameters are required for tone correction processing, then processing precision is improved, but ease of operation deteriorates
Solution Approach 1:
The convertor acts as an intermediary that automatically handles the transformation from simple user-input parameters to complex processing parameters. Users only need to provide high-level instructions through the first input parameters, and the convertor automatically generates the appropriate second input parameters with correct values and settings, eliminating the need for users to understand or manually configure multiple complex parameters.
Solution Approach 2:
The system performs self-service by automatically generating the complex second input parameters from the simple first input parameters without requiring user intervention or expertise. The convertor autonomously completes the parameter transformation task, allowing users to operate the system easily while maintaining high processing precision through the use of multiple parameters.
Data Source
AI summary
GUI of an image processing device according to the present invention is a control window which receives a change of an input parameter of spatial tone correction processing and receives an operation of changing five input parameters. In GUI, first input parameters such as a correction method, correction strength, a degree of color enhancement, brightness, and a correction range can be set. The correction method is selected through pull-down menu, and any one or a plurality of methods can be selected from four correction methods such as contrast enhancement, backlight correction, dark-portion correction, and bright-portion correction. The correction strength, degree of color enhancement, brightness, and correction range are designated through slide-bars of which values are respectively set by a user moving sliders within the window in a transverse direction. Value displays display designated values of the correction strength, the degree of color enhancement, the brightness, and the correction range.