Display Control Chip Adaptive Image Optimization
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Solution Overview
Problem
Current display technologies require manual user input to adjust display modes, which is inefficient and time-consuming, as they do not automatically adapt to the usage situation based on the content being displayed.
Innovation Solution
A method and display control chip that analyze pixel distribution across sub-areas of an image to determine if specific target patterns are present, allowing for adaptive image processing to optimize the display mode automatically, reducing the need for manual adjustments and improving user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual push buttons are used to adjust display modes, then users can select different display modes for different situations, but the operation becomes ineffective and time-consuming
Solution Approach 1:
The display control chip automatically analyzes the video signal content and identifies target patterns (such as game interfaces, video playback, or desktop environments) without requiring user input. Based on the identified content type, the chip automatically adjusts display parameters including chromaticity, contrast, and brightness, enabling the display system to serve itself by making adaptive decisions without manual intervention
Solution Approach 2:
The system continuously monitors the video signal input and uses the pixel number distribution information as feedback to determine the current display scenario. This feedback mechanism allows the display control chip to dynamically adjust display modes in real-time based on the actual content being displayed, creating a closed-loop adaptive system
2Measurement precision
If complete images are stored for analysis, then accurate content recognition can be achieved, but the circuit area and storage requirements increase
Solution Approach 1:
Instead of storing or processing complete images, the system extracts only the essential feature information - specifically the pixel number distribution across different gray level ranges (0-31, 32-63, 64-127, 128-191, 192-255). This extraction approach captures the critical characteristics needed for content recognition while eliminating redundant data, significantly reducing the computational and storage requirements
Solution Approach 2:
The image analysis is segmented into discrete gray level ranges, with each range being independently analyzed to count pixel numbers. This segmentation transforms the continuous image data into discrete, manageable categories that can be processed efficiently without requiring full image storage or complex processing
Data Source
AI summary
A method for optimizing a display image based on display content is provided. The method is applicable to a display control chip, and includes following operations: receiving a video signal configured to transmit an image of a frame; with respect to multiple different sub-areas in an area of the image, calculating a pixel number distribution of each sub-area along multiple characteristic values; determining, according to the pixel number distribution, whether the sub-area comprises a corresponding first target pattern of multiple first target patterns; if the multiple sub-areas comprise the multiple first target patterns, respectively, performing a first preset image processing to the image to generate a processed image; if the multiple sub-areas are free from comprising the multiple first target patterns, respectively, omitting the first preset image processing to the image; and generating a display signal according to the processed image or the image.