Video Metadata Analysis for Automated Editing and Summarization
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Solution Overview
Problem
Existing video editing techniques are cumbersome and time-consuming, often requiring manual processing without consideration for dependencies among various techniques, and fail to effectively address redundant, poorly filmed, or unintended sections in captured video.
Innovation Solution
The generation and analysis of video metadata, including content-based and non-content-based metadata, to prioritize and classify video segments, allowing for automated editing suggestions and summary video creation, which can be performed in real-time by image signal processors or during post-processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual video editing techniques are applied one-at-a-time, then individual video characteristics can be improved, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The patent segments video editing into multiple independent analysis techniques (stability analysis, exposure analysis, redundancy detection, etc.), each generating metadata scores for different video portions. These segmented analyses can be performed in parallel and combined to produce comprehensive editing decisions, resolving the contradiction between thorough video quality improvement and time-consuming sequential processing
Solution Approach 2:
The patent performs preliminary analysis of video content during or immediately after capture, generating metadata scores and identifying undesirable portions before the actual editing process begins. This preliminary action includes analyzing stability, exposure, and redundancy characteristics, allowing the editing process to proceed more efficiently by pre-identifying which portions need modification
Solution Approach 3:
The patent transforms video quality assessment from subjective manual judgment to objective parameter-based scoring by generating metadata that quantifies various characteristics (stability scores, exposure scores, redundancy scores). These parameter changes enable automated decision-making and streamline the editing process while maintaining high video quality standards
2Reliability
If comprehensive video analysis techniques are applied to address all video defects, then video quality improves, but the process becomes unwieldy and complex
Solution Approach 1:
The patent creates a universal video analysis framework where a single system performs multiple analysis functions (stability analysis, exposure analysis, redundancy detection, scene change detection) through different metadata generation techniques. This multi-functional approach improves video quality comprehensively while managing complexity by providing a unified processing architecture that handles all analysis types consistently
Solution Approach 2:
The patent introduces metadata as an intermediary layer between raw video content and editing decisions. Instead of directly manipulating complex video data, the system generates metadata scores that represent various quality characteristics, then uses these intermediate metadata values to guide editing decisions. This intermediary approach simplifies the overall process by decoupling analysis from execution
3Productivity
If automated video analysis and editing is implemented, then processing time reduces, but the system complexity increases
Solution Approach 1:
The patent implements self-service automation where the video analysis system automatically generates metadata, identifies undesirable portions, and produces editing suggestions without requiring manual intervention at each step. The system serves itself by using its own generated metadata to drive the editing decision-making process, significantly improving productivity while the automation handles the complexity internally
Solution Approach 2:
The patent incorporates feedback mechanisms where metadata scores from video analysis are fed back into the editing decision process. The system continuously monitors video characteristics, generates scores, and uses these scores to automatically adjust editing decisions. This feedback loop enables automated productivity improvement while managing system complexity through iterative refinement based on measured performance
Data Source
AI summary
Systems and processes for improved video editing, summarization and navigation based on generation and analysis of metadata are described. The metadata may be content-based (e.g., differences between neighboring frames, exposure data, key frame identification data, motion data, or face detection data) or non-content-based (e.g., exposure, focus, location, time) and used to prioritize and/or classify portions of video. The metadata may be generated at the time of image capture or during post-processing. Prioritization information, such as a score for various portions of the image data may be based on the metadata and/or image data. Classification information such as the type or quality of a scene may be determined based on the metadata and/or image data. The classification and prioritization information may be metadata and may be used to automatically remove undesirable portions of the video, generate suggestions during editing or automatically generate summary video.