Content-Driven Transcoding for Error Resilient Multimedia
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Solution Overview
Problem
Existing video transcoding technologies lack efficient processing and compression methods that utilize content information for error resilience and scalability in multimedia data applications, particularly in mobile broadcasting.
Innovation Solution
A content-driven transcoding approach that uses metadata and calculated content metrics to classify multimedia data, enabling adaptive encoding techniques such as bit rate control, motion estimation, and error resilience schemes, while aligning data boundaries with frame boundaries for optimal quality maintenance across channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional video transcoding is used without content information, then processing is simpler, but coding efficiency and error resiliency are insufficient
Solution Approach 1:
The patent applies preliminary action by calculating content information (motion activity, spatial complexity, temporal complexity) before the encoding process. This pre-computed content information is then used to guide encoding decisions, allowing the system to proactively adjust encoding parameters based on scene characteristics rather than reactively responding to errors during transmission.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting encoding parameters based on content information. Specifically, it varies intra-refresh rates, quantization parameters, and coding modes according to measured motion activity and spatial/temporal complexity metrics, allowing the encoder to optimize for error resiliency in high-motion scenes while maintaining efficiency in stable scenes.
2Productivity
If adaptive encoding based on content information is implemented, then coding efficiency improves, but processing complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the video content into distinct regions or scenes based on content information metrics. Different encoding strategies are applied to different segments - for example, high-motion regions receive different treatment than low-motion regions, allowing efficient resource allocation while maintaining overall coding efficiency.
Solution Approach 2:
The patent implements self-service by using the video content itself to generate the content information needed for encoding decisions. The encoder analyzes its own input stream to extract motion activity, spatial complexity, and temporal complexity metrics, eliminating the need for external side-information channels or pre-processing systems.
3Manufacturing precision
If data boundaries are misaligned with frame boundaries, then processing flexibility is higher, but quality maintenance across channels deteriorates
Solution Approach 1:
The patent applies dynamics by making the boundary alignment adaptive rather than fixed. The encoder dynamically adjusts data boundary placement based on content information and channel characteristics, allowing optimal quality maintenance for different content types and transmission conditions while preserving processing flexibility through content-aware adaptation.
Data Source
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AI summary
A method of increasing the error resilience of encoded multimedia data, the method comprising obtaining content information of multimedia data, wherein the content information includes content classifications for portions of the multimedia data, each content classification for a portion determined by a texture value associated with spatial complexity of the portion, and a motion value associated with temporal complexity of the portion; and encoding portions of the multimedia data as intra-coded blocks or inter-coded blocks based on the content classification.