Immersive Media Motion Metadata for Selective Comfort Compensation
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Solution Overview
Problem
Existing media streaming technologies fail to address user comfort issues related to high motion content, particularly in immersive media experiences, leading to varying user experiences and potential discomfort.
Innovation Solution
A metadata track encoding motion and comfort information is provided to ensure that immersive media content meets user-specific comfort levels by allowing real-time feedback and corrective actions during production and playback, such as scene replacement or adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If immersive media content is provided without motion comfort metadata, then the media streaming system is simpler, but user comfort deteriorates due to high motion content causing discomfort
Solution Approach 1:
The system performs preliminary analysis of motion characteristics during media production and encodes comfort metrics into metadata before playback. This allows the playback device to prepare appropriate comfort adjustments in advance without adding complexity to the core streaming infrastructure, as the heavy computation occurs during production rather than consumption.
Solution Approach 2:
Motion comfort metadata acts as an intermediary layer between the media content and the playback system. This metadata track carries comfort information separately from the main media stream, allowing the playback device to interpret and apply comfort adjustments without fundamentally redesigning the streaming protocol or media format.
2Object-affected harmful factors
If motion compensation is performed for every scene, then user comfort is improved, but processing time and computational resources increase
Solution Approach 1:
Instead of applying motion compensation uniformly to all scenes, the system uses comfort metrics to identify only the specific scenes or time segments where motion discomfort is likely to occur. The playback device then applies compensation selectively to those portions, reducing overall processing time while maintaining comfort where it matters most.
Solution Approach 2:
The system pre-identifies high-motion segments and marks them in the metadata during production. This allows the playback device to skip unnecessary motion analysis and compensation processing for low-motion scenes, applying computational resources only to the critical segments identified in advance.
3Adaptability or versatility
If personalized comfort levels are implemented, then user experience is improved, but system complexity and data requirements increase
Solution Approach 1:
Instead of creating entirely new personalized media streams for each user, the system uses comfort metrics as a template or copy that can be applied universally. The same comfort adjustment algorithms and metadata interpretation methods work for all users, with only the threshold parameters needing customization, thereby avoiding the complexity of user-specific processing pipelines.
Solution Approach 2:
The system maintains a standardized media delivery architecture but allows flexibility through parameter changes in comfort thresholds and adjustment intensity. Different users can have personalized experiences by modifying these parameters rather than requiring fundamental system reconfiguration, achieving adaptability without proportionally increasing complexity.
Data Source
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AI summary
Techniques for managing motion data for an immersive media item include receiving media data and corresponding motion data, and determining whether a motion compensation trigger is satisfied. This trigger could be based on a personal motion compensation threshold of a viewer, one or more physical characteristics of the viewer, or a user-selected motion level. If the motion compensation trigger is satisfied, a corrective action is performed on the media data to obtain adjusted media data, which is then presented on a display of an electronic device. The corrective action involves determining a prescriptive motion based on the motion data and applying this prescriptive motion to the media data.