VideoLens Media Engine Real-Time Metadata Generation
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Solution Overview
Problem
Current methods for enhancing the online video experience of free, user-generated content are inefficient due to the complexity of audio/video analysis techniques, the high encoding complexity of codec technologies, and the failure of industry standards like MPEG-7 and MPEG-21 to address real-time metadata creation, making it economically unrealistic to provide a rich viewing experience similar to premium content.
Innovation Solution
The VideoLens Media Engine performs real-time analysis of multimedia data to identify action, calm, and transition scenes using audio and video features, generating metadata that can be used for customized playback, tagging, and sharing, and is designed to operate on resource-limited consumer devices, leveraging FFMPEG for efficient processing and portability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated audio/video analysis methods are used to generate metadata for free content, then metadata generation becomes scalable and economically viable, but the complexity of the analysis techniques limits real-time operation and requires excessive computing resources
Solution Approach 1:
The patent segments the video stream into discrete frames and processes only selected key frames for metadata generation. By dividing the continuous video data into manageable frame units and applying analysis selectively rather than continuously, the system achieves automated metadata generation while reducing the computational burden on consumer devices.
2Extent of automation
If codec technology is used to embed key information during encoding, then metadata generation is automated, but the encoding and decoding process becomes highly complex
Solution Approach 1:
The patent extracts essential visual features from video frames and embeds them as lightweight metadata side-car files rather than integrating complex encoding/decoding mechanisms into the codec itself. This approach automates metadata generation while avoiding the high complexity of codec-based solutions by separating the metadata extraction process from the core video encoding/decoding operations.
3Adaptability or versatility
If industry standards like MPEG-7 and MPEG-21 are used to enable search and sharing, then a framework for information exchange is established, but the fundamental issue of key information extraction from content is not addressed
Solution Approach 1:
The patent performs preliminary extraction of visual features and generates metadata side-car files before the actual video playback or search operations. By preparing the metadata in advance through automated analysis of key frames, the system establishes the information extraction foundation that enables subsequent search and sharing operations using standard protocols like MPEG-7 and MPEG-21.
4Ease of operation
If manual metadata creation is used for premium content, then a rich and interactive viewing experience is provided, but the process does not scale for free user-generated content
Solution Approach 1:
The patent implements self-service automated metadata generation where the system extracts visual features and generates metadata side-car files automatically without human intervention. This allows free user-generated content to receive the same quality of metadata enrichment as premium content, enabling scalable processing while maintaining ease of operation for both content creators and consumers.
Data Source
AI summary
A system, method, and computer program product for automatically analyzing multimedia data are disclosed. Embodiments receive multimedia data, detect portions having specified features, and output a corresponding subset of the multimedia data. Content features from downloaded or streaming movies or video clips are identified as a human probably would do, but in essentially real time. Embodiments then generate an index or menu based on individual consumer preferences. Consumers can peruse the index, or produce customized trailers, or edit and tag content with metadata as desired. The tool can categorize and cluster content by feature, to assemble a library of scenes or scene clusters according to user-selected criteria.