Attention-Adaptive Stream Decoding for Lower-Power Interactive Playback
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Solution Overview
Problem
Streaming systems for interactive entertainment impose computational and bandwidth overheads on client devices, particularly battery-powered devices, due to conventional image encoding schemes, which can exclude lower-capability devices and reduce battery life.
Innovation Solution
A client device with an attention estimation processor adjusts decoding quality based on user engagement, and a server encodes content responsive to user interaction, using a multi-stage neural network decoder and encoder to optimize computational and bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image encoding schemes are used for streaming, then content quality can be maintained, but computational load and bandwidth overhead increase significantly
Solution Approach 1:
The system dynamically adjusts encoding and decoding quality based on real-time user attention estimation. When user attention is low, the system reduces encoding/decoding quality to decrease computational load and energy consumption. When attention is high, quality is maintained or improved. This dynamic adaptation resolves the contradiction by making quality flexible rather than fixed.
Solution Approach 2:
The patent changes key parameters (encoding quality, decoding precision) based on user attention state. The attention estimation processor generates attention scores that directly control the encoding/decoding parameter selection, allowing the system to optimize the balance between content quality and computational energy consumption in real-time.
2Manufacturing precision
If high-quality decoding is performed continuously, then content quality is maintained, but battery life is reduced
Solution Approach 1:
Instead of continuous high-quality decoding, the system uses periodic attention estimation to determine when high-quality decoding is necessary. The attention estimation processor periodically assesses user attention and adjusts decoding quality accordingly, maintaining high quality only during periods of detected user engagement while using lower quality decoding during disengagement periods, thus extending battery life.
Solution Approach 2:
The decoding quality is made dynamic rather than static, adjusting in real-time based on user attention state. This allows the system to extend battery life by reducing decoding quality during periods when users are not actively engaged, while maintaining high quality when needed, resolving the contradiction between continuous quality and battery conservation.
3Reliability
If minimum performance requirements are set high for client devices, then streaming quality can be guaranteed, but device accessibility is reduced
Solution Approach 1:
The system changes performance parameters dynamically based on user attention rather than requiring consistently high performance. This allows lower-capability devices to stream content effectively by reducing processing demands during low-attention periods while maintaining quality during high-attention periods, thereby improving device accessibility without sacrificing streaming quality guarantees.
Solution Approach 2:
The patent introduces dynamic performance requirements that adapt to user engagement levels. Instead of imposing static minimum performance thresholds that exclude lower-capability devices, the system adjusts processing requirements in real-time based on attention estimation, enabling a broader range of devices to access the streaming service while maintaining quality when users are actively engaged.
Data Source
AI summary
A method of displaying streamed content includes receiving streamed content from a server, decoding the streamed content, outputting the decoded streamed content for display, and estimating a degree of attention of a user on the streamed content, where the decoding step comprises decoding the content to a quality that is dependent upon the estimated degree of attention of the use.