Video Display Device Using Dual Retinex Processing for Visibility
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Solution Overview
Problem
Existing video display technologies fail to effectively adjust contrast and dynamic range compression based on the specific features and reflection properties of objects in video signals, leading to uniform correction that does not enhance visibility across different video scenes.
Innovation Solution
A video display device that employs a combination of Retinex processing units, including the McCann 99 model for illumination estimation and the Multiscale Retinex model for contrast correction, along with a feature analyzing unit to adjust video composition based on luminance levels and reflection properties, allowing for dynamic range compression that enhances visibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform contrast correction is applied to all video scenes, then the processing complexity is reduced, but the visibility improvement is insufficient for different video features
Solution Approach 1:
The patent applies different contrast correction strengths to different regions of the video image based on local feature analysis. The feature analysis unit identifies regions with different characteristics (such as skin tones, natural scenes, action scenes) and applies appropriately differentiated correction, rather than uniform correction across the entire image.
Solution Approach 2:
The system dynamically adjusts correction parameters based on detected video features. The contrast correction amount, gamma correction values, and other parameters are automatically modified according to the analyzed scene characteristics, enabling adaptive optimization without manual intervention.
2Manufacturing precision
If feature analysis and differentiated correction are applied, then the visibility improvement precision is enhanced, but the processing complexity increases
Solution Approach 1:
The video processing system is divided into distinct functional modules: a feature analysis unit that identifies scene characteristics, a correction determination unit that selects appropriate correction strategies, and an image processing unit that applies the corrections. This modular segmentation allows each component to perform its specific function efficiently.
Solution Approach 2:
The system performs automatic feature analysis and self-adjusts correction parameters without requiring manual intervention. The feature analysis unit autonomously identifies scene types, and the correction determination unit automatically selects and applies appropriate correction settings, enabling the system to serve itself in optimizing video quality.
3Speed
If dynamic range compression is applied without considering reflection properties, then the processing speed is maintained, but the naturalness of the enhanced video is reduced
Solution Approach 1:
The system dynamically adapts the dynamic range compression process based on detected reflection properties of objects in the video. Rather than applying fixed compression parameters, the system adjusts compression strength and characteristics in real-time according to the reflective properties identified in different regions, preserving natural appearance while maintaining processing efficiency.
Data Source
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AI summary
A more favorably visibility-improved video is obtained. A device includes: a video input unit; a first Retinex processing unit which performs a first Retinex process on a video inputted from the video input unit; a second Retinex processing unit which performs a second Retinex process, which is different from the first Retinex process in a method, on the video inputted from the video input unit; a video composing unit which can compose a video processed by the first Retinex processing unit and a video processed by the second Retinex processing unit in accordance with a feature of the video inputted from the video input unit; and a display unit which can display an output video of the video composing unit.