Display Power Savings Aggressiveness Control via Machine Learning
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Solution Overview
Problem
Existing display power saving technologies suffer from inconsistent image fidelity, visual artifacts, and limited power savings in certain usage scenarios, such as web browsing, due to their inability to dynamically adjust aggressiveness levels based on content.
Innovation Solution
The use of machine learning to dynamically adjust the aggressiveness level of display power saving algorithms, such as DPST and CABC, based on the content, to maximize power savings while maintaining acceptable image fidelity, as measured by a mean opinion score (MOS) threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If display power saving algorithms (DPST, CABC) are applied to reduce power consumption, then power savings are achieved, but image fidelity deteriorates and visual artifacts appear
Solution Approach 1:
The patent implements dynamic adjustment of aggressiveness levels based on content analysis. The system transitions from static power saving settings to dynamic control where the aggressiveness level changes according to the specific content being displayed, allowing optimal balance between power savings and image fidelity for different scenarios
Solution Approach 2:
The system changes the parameter of aggressiveness level (from fixed to variable) to resolve the contradiction. By adjusting this parameter dynamically based on content characteristics, the system achieves both power savings and acceptable image fidelity, preventing the trade-off from becoming a fixed compromise
2Use of energy by moving object
If high aggressiveness level is used for maximum power savings, then power consumption is reduced, but visual artifacts such as color banding and rolling effects increase
Solution Approach 1:
The system incorporates feedback mechanisms where the output (displayed image with power saving applied) is evaluated against quality thresholds. If visual artifacts exceed acceptable levels, the system adjusts the aggressiveness level down, creating a closed-loop control that prevents harmful artifacts while maintaining power savings
Solution Approach 2:
The system takes preliminary action by analyzing content characteristics before applying power saving algorithms. By predicting which content types are susceptible to artifacts and pre-adjusting aggressiveness levels accordingly, the system prevents visual artifacts before they occur rather than correcting them afterward
3Device complexity
If fixed aggressiveness level is used for power saving algorithms, then device complexity is reduced, but adaptability to different content types deteriorates
Solution Approach 1:
The system applies different aggressiveness levels to different content types (local optimization) rather than using a uniform setting. Web browsing content receives different treatment than video content or images, allowing each content type to get the optimal aggressiveness level for its specific characteristics
Solution Approach 2:
The system performs self-analysis of content characteristics and automatically adjusts aggressiveness levels without requiring manual user input or complex external control. The display controller itself evaluates the content and makes intelligent decisions about power saving aggressiveness, reducing the need for complex external control mechanisms
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture to control an aggressiveness of display panel power savings are disclosed. An example apparatus include a display panel, a display controller to adjust an image to be displayed by the display panel, and a power savings controller to access an image provided to the display controller, execute a machine learning model using the image as an input to generate an aggressiveness value, and provide the aggressiveness value to the display controller, the display controller to adjust the image based on the aggressiveness value.