Cross-Asset Media Analysis for Quality Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image analysis and processing techniques rely solely on pixel analysis, which can be inadequate when dealing with media assets captured from different devices or at varying times, leading to inconsistencies in quality, viewpoint, and object detection.
Innovation Solution
A system and method that cross-analyze media assets by deriving characteristics from a first media asset, searching for correlated assets, and applying content corrections based on metadata to improve quality, processing, and decoding, utilizing techniques such as motion blur correction, facial recognition, and panoramic image stitching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pixel analysis is used for image processing, then processing speed is maintained, but processing accuracy and quality improvement are limited when dealing with multiple media assets captured from different devices and times
Solution Approach 1:
The system segments the processing task into distinct modules: a correlation module that identifies relationships between media assets based on metadata (time, location, device identifiers), and a processing module that applies corrections. This segmentation allows the system to handle complexity systematically while improving accuracy through cross-asset analysis.
Solution Approach 2:
Metadata serves as an intermediary that bridges different media assets captured from various devices and times. By using metadata (capture time, location, device identifiers) as the mediating element, the system can correlate assets without direct pixel-level comparison, improving processing accuracy while managing system complexity.
2Reliability
If multiple media assets are analyzed and correlated, then processing quality and object detection improve, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary correlation analysis using metadata (capture time, location, device identifiers) before executing the main processing tasks. By pre-identifying which assets are correlated and should be processed together, the system avoids unnecessary computations on unrelated assets, reducing overall processing time while maintaining quality consistency.
Solution Approach 2:
The system applies partial action by selectively processing only the specific characteristics that need correction (e.g., motion blur, exposure) in correlated assets rather than performing complete re-processing. This targeted approach improves reliability for specific quality parameters while minimizing the time and computational resources required.
3Manufacturing precision
If cross-asset analysis is performed to correct media assets, then image quality and object detection improve, but device complexity and processing overhead increase
Solution Approach 1:
The correlation module serves multiple functions: it identifies correlated assets, extracts relevant metadata, and determines processing priorities. This multi-functionality reduces the need for separate specialized modules, managing device complexity while enabling comprehensive cross-asset analysis to improve image quality and object detection.
Solution Approach 2:
The system uses the metadata that is already embedded in the media assets (capture time, location, device identifiers) to perform self-correlation and self-processing decisions. This self-service approach minimizes the need for external processing overhead while maintaining high image quality through cross-asset corrections.
Data Source
AI summary
A method for processing media assets includes, given a first media asset, deriving characteristics from the first media asset, searching for other media assets having characteristics that correlate to the characteristics of the first media asset, when a match is found, deriving content corrections for the first media asset or a matching media asset from the other of the first media asset or the matching media asset, and correcting content of the first media asset or the matching media asset based on the content corrections.