Staged Content Analysis for Multimedia Redundancy
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Solution Overview
Problem
Current methods for analyzing multimedia content are inefficient and slow, as they perform frame-by-frame analysis without exploiting redundancies, making the process tedious and time-consuming.
Innovation Solution
A staged content analysis method that performs a high-level analysis on multiple streams of multimedia content, segregates portions with detected general features for specialized analysis, and correlates the results to generate a weighted content description, thereby speeding up the process by identifying and filtering out redundant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frame-by-frame analysis is performed on a single stream, then comprehensive content analysis is achieved, but the process becomes tedious, inefficient and slow
Solution Approach 1:
The patent segments the content analysis process into multiple stages: a first stage performing coarse analysis on multiple streams to identify general features, and a second stage performing specialized analysis only on portions containing those features. This segmentation allows the system to maintain comprehensive analysis accuracy while dramatically improving processing speed by avoiding redundant frame-by-frame analysis of all content.
Solution Approach 2:
The patent performs preliminary coarse analysis on multiple streams before conducting detailed specialized analysis. By identifying general features in advance and filtering out portions without these features, the system prepares the data in a way that enables faster subsequent processing while maintaining analysis completeness.
2Reliability
If frame-by-frame analysis is performed on a single stream, then detailed content features are detected, but the process is time-consuming and inefficient
Solution Approach 1:
The analysis process is divided into two segments: a first stage that quickly scans multiple streams for general features, and a second stage that performs detailed specialized analysis only on relevant portions. This ensures reliable feature detection is maintained while significantly reducing the time spent on redundant analysis of content without target features.
Solution Approach 2:
Instead of performing full specialized analysis on all content, the patent applies partial action by conducting detailed analysis only on portions that contain the general features identified in the first stage. This partial approach maintains detection reliability for relevant content while avoiding unnecessary analysis time on irrelevant portions.
3Loss of information
If multiple streams are analyzed without segregation, then all content is processed, but redundancies are not exploited and efficiency is reduced
Solution Approach 1:
The patent segments the multiple streams into portions containing general features and portions without such features. By separating these streams and applying specialized analysis only to relevant portions, the system maintains complete content coverage for feature detection while exploiting redundancies to improve processing efficiency.
Solution Approach 2:
The patent extracts and separates portions of content that contain general features from the rest of the multiple streams. This extraction allows the system to focus detailed analysis only on relevant content, eliminating redundant processing of content without target features while ensuring no relevant information is lost.
Data Source
AI summary
A system that incorporates teachings of the present disclosure may include, for example network device having a controller to receive multiple streams of content for portions of a multimedia work, perform a high level analysis for features in each of the streams for the multimedia work, perform a specialized analysis on the portion having a detected general feature to generate a content analysis output, correlate the content analysis output with other content analysis of the multimedia work, and output a weighted content description based on the correlation function. Other embodiments are disclosed.