Video Contrast Adjustment via Reference Image Comparison
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Solution Overview
Problem
Existing methods for adjusting the contrast value of a video image, whether through software or at the video capturing end, face challenges in achieving optimal results due to reliance on human judgment or insufficient content information, leading to suboptimal image quality.
Innovation Solution
A method where a software part determines the image quality by comparing the video image with a reference image using histogram equalization and structural similarity evaluation, and adjusts the contrast value of the video capturing end through a firmware part, dynamically adjusting the contrast value to improve image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If software adjusting method is used to adjust the contrast value of a video image, then the adjustment can be made, but the adjustment result cannot meet the requirement when the video image has a lower contrast ratio because the content information of the original video image is not enough
Solution Approach 1:
The patent applies preliminary action by using histogram equalization to process the original video image and generate a reference image before contrast adjustment. This reference image contains enhanced content information that is then used to guide the contrast adjustment process, ensuring that important content details are preserved while improving overall contrast.
Solution Approach 2:
The patent implements feedback by comparing the original video image with the reference image using SSIM evaluation to generate an image evaluation value. This evaluation feedback is used to determine whether contrast adjustment is needed and to guide the adjustment process, ensuring that content information is preserved while improving contrast.
2Ease of operation
If another method of directly adjusting the contrast value of the video capturing end is provided, then the contrast can be adjusted, but the adjustment result is not always correct because the eyes of human are used to determine the adjustment result
Solution Approach 1:
The patent replaces the mechanical system of human visual judgment with an automated image processing system. Histogram equalization and SSIM-based evaluation algorithms automatically determine the optimal contrast adjustment, eliminating subjectivity and inconsistency while maintaining operational simplicity through automated firmware execution.
Solution Approach 2:
The patent enables self-service by implementing an automated contrast adjustment system that processes images independently without requiring human intervention. The firmware automatically evaluates images using SSIM, determines optimal contrast values, and applies adjustments, making the system self-sufficient and consistent.
3Manufacturing precision
If it is very difficult to use the above methods to improve the contrast value, then the current methods have limitations, but a new method is needed to dynamically change the contrast value to improve the image quality
Solution Approach 1:
The patent segments the contrast adjustment system into distinct functional modules: histogram equalization for reference image generation, SSIM evaluation for image quality assessment, and firmware-based contrast adjustment execution. This modular segmentation manages complexity while achieving high image quality through coordinated operation of specialized components.
Solution Approach 2:
The patent introduces a reference image as an intermediary element that mediates between the original video image and the final contrast-adjusted image. The reference image, generated through histogram equalization, serves as a guide for preserving content information while improving contrast, effectively bridging the gap between original and adjusted images.
Data Source
AI summary
A method for adjusting a contrast value of a video capturing end to a optimum contrast value is provided. Firstly, a first video image and a first contrast value of the first video image are gathered by a software part. A reference image is generated according to the first video image. The first video image is compared with the reference image to generate a first image evaluation value and an operation is performed to determine whether the first image evaluation value is greater than a first threshold. When the first image evaluation value is smaller than the first threshold, a second contrast value is generated according to the first contrast value and a dynamical contrast adjusting value. The second contrast value is sent to a firmware part by the software part to adjust a contrast value of the video capturing end according to the second contrast value.