Video Encoder Adjusts Quality Based on User Attention
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Solution Overview
Problem
Video encoders consume significant resources and bandwidth, even when users are not paying attention to the video stream, leading to inefficient resource utilization and potential quality issues.
Innovation Solution
An encoding adjustment application that detects user attention and anticipated attention by analyzing saccadic events and user inattention, adjusting the video encoder to reduce resource consumption by lowering encoding quality or stopping encoding during periods of inattention, leveraging the saccadic masking phenomenon to minimize perceived quality degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video encoding is performed continuously at high quality, then video quality is maintained, but processing resources and network bandwidth are wasted during user inattention
Solution Approach 1:
The video encoder dynamically adjusts its operation mode based on real-time detection of user attention state. When user attention is detected, the encoder operates at high quality settings; when inattention is detected, it reduces or stops encoding. This dynamic adaptation resolves the contradiction by making encoding intensity variable rather than fixed, allowing quality maintenance during attention while conserving resources during inattention
Solution Approach 2:
The system implements a feedback loop where user attention state is continuously monitored and fed back to control the video encoder's operation. The attention detection module provides real-time feedback about user engagement level, which the encoder uses to adjust its encoding intensity. This closed-loop control ensures quality is maintained only when needed, eliminating wasteful resource consumption during inattention periods
2Reliability
If video encoding is performed continuously at high quality, then video quality is maintained, but network bandwidth is consumed unnecessarily during user inattention
Solution Approach 1:
The system dynamically adjusts network bandwidth consumption by modulating video encoding intensity based on user attention state. During user attention, encoding proceeds at full quality with corresponding bandwidth usage. During inattention, encoding is reduced or halted, automatically scaling bandwidth consumption to match actual user needs. This dynamic resource allocation eliminates unnecessary bandwidth consumption while preserving quality when required
Solution Approach 2:
User attention detection provides real-time feedback that controls bandwidth allocation for video streaming. The system monitors attention metrics and adjusts encoding output accordingly, creating a feedback-driven bandwidth management mechanism. This ensures network resources are consumed only when users are actively viewing, preventing wasteful bandwidth usage during inattention while maintaining quality during engagement
3Manufacturing precision
If video encoding uses high processing resources, then encoding quality is improved, but resource efficiency deteriorates during periods of user inattention
Solution Approach 1:
The video encoder transitions from static high-resource operation to dynamic resource allocation based on user attention detection. Encoding quality settings are adjusted in real-time: high processing resources are allocated during user attention to maintain encoding quality, while resources are reduced or reallocated during inattention periods. This dynamic resource management resolves the contradiction by making resource consumption proportional to actual user needs rather than constantly high
Solution Approach 2:
The system changes key encoding parameters (such as bitrate, resolution, frame rate) based on detected user attention state. During attention, parameters are set for high quality output requiring substantial processing resources. During inattention, parameters are reduced or encoding is suspended, dramatically lowering resource consumption. This parameter adaptation allows the system to maintain encoding quality when needed while improving resource efficiency during inattention
Data Source
AI summary
Disclosed are various embodiments for adjusting the encoding of a video signal into a video stream based on user attention. A temporary lapse of attention by a user is predicted. The encoding of the video signal into the video stream is adjusted from an initial state to a conservation state in response to predicting the temporary lapse of attention by the user. The conservation state is configured to conserve one or more resources used for the video stream relative to the initial state.