Machine-Learning Frame Processing for Consistent Gaming Reaction Times
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Solution Overview
Problem
Conventional systems apply static updates to visual characteristics of frames, which can decrease reaction times for some frames but increase them for others, leading to inconsistent user response times in gaming applications.
Innovation Solution
Utilize machine learning models to dynamically update visual characteristics such as contrast, brightness, and saturation levels of frames, processing individual frames or groups separately to minimize reaction times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If static updates to visual characteristics are applied to all frames, then reaction times decrease for some frames, but reaction times increase for other frames
Solution Approach 1:
The patent applies dynamics by transitioning from static visual characteristic updates to dynamic updates. The machine learning model analyzes each frame individually and adjusts visual characteristics (contrast, brightness, saturation) in real-time based on the specific content and requirements of each frame, allowing the system to adapt visual properties dynamically rather than applying a fixed transformation to all frames
Solution Approach 2:
The patent implements local quality by processing frames individually through the machine learning model rather than applying a uniform transformation. Each frame receives customized visual characteristic adjustments based on its specific content, ensuring that events in each frame are optimized for observability and reaction time without compromising other frames
2Loss of time
If machine learning models process frames dynamically, then reaction times are minimized for majority of frames, but processing complexity increases
Solution Approach 1:
The machine learning model performs self-service by automatically analyzing frame content and determining optimal visual characteristics without requiring manual intervention or complex user input. The model processes frames autonomously, making intelligent decisions about contrast, brightness, and saturation adjustments based on the frame content itself
Solution Approach 2:
The patent applies parameter changes by modifying visual characteristics (contrast, brightness, saturation) of frames based on machine learning analysis. The model adjusts these parameters dynamically for each frame or group of frames, transforming the visual properties to optimize reaction times while managing processing complexity through efficient parameter transformation
Data Source
AI summary
In various examples, model-based processing to reduce reaction times for content streaming systems and applications is described herein. Systems and methods are disclosed that use one or more machine learning models to process image data representative of frames of an application, such as a gaming application, in order to generate updated imaged data representative of one or more updated frames that help reduce reaction times for users. For instance, the machine learning model(s) may update one or more visual characteristics associated with the frames, such as a contrast, a brightness, and/or a saturation associated with the frames. As described herein, the machine learning model(s) may be trained to update the frames in order to reduce the reaction times of users, such as by using one or more loss functions that measure loss in predicted reactions times and/or loss associated with visual characteristics of frames.