Video Frame Difference Engine for Edit History Tracking
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Solution Overview
Problem
In video post-production, preserving intermediate edits is inefficient due to large file sizes, and existing methods are sensitive to image quality and transformations, making it difficult to track frame changes effectively.
Innovation Solution
The solution employs image similarity techniques, such as discrete cosine transform (DCT) encoding and image hashing, to identify frame differences by comparing frame signatures, allowing for the detection of added, deleted, or updated frames in a video, while reducing the need to store entire intermediate files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If intermediate video edits are preserved to maintain editing history, then editing tracking capability is improved, but storage space consumption increases significantly
Solution Approach 1:
The patent segments the video editing tracking problem into two parts: (1) storing only the difference data (added, deleted, modified frames) rather than complete intermediate videos, and (2) storing metadata descriptions of edits. This segmentation allows preserving editing history while minimizing storage space by only storing changes rather than entire video files.
Solution Approach 2:
The patent creates simplified copies of video data in the form of frame difference representations and metadata, rather than storing complete intermediate video files. These copies contain only the essential editing information needed to reconstruct the editing history, significantly reducing storage requirements while maintaining the ability to track edits.
2Measurement precision
If frame comparison methods are sensitive to image quality and transformations, then measurement precision is improved, but reliability deteriorates due to false positives
Solution Approach 1:
The patent changes the parameters used for frame comparison from direct pixel-level comparison to metadata-based comparison and perceptual hashing. By transforming the comparison parameters to be more robust to common video transformations (compression, resizing, format conversion), the system maintains measurement precision while improving reliability across different video quality conditions.
Data Source
AI summary
An embodiment detects where frames within a video have been added, deleted, or changed. The embodiment tracks the modifications through subsequent versions of the source video, and retains various metadata (corresponding to the individual edits) that describes the context and specifics of the edits. In an embodiment only changes to the original video need be tracked by preserving only those clips that have been added, removed, or changed. This ability enables a video editor to maintain the various changes made to a video and see the history of changes while alleviating much of the file storage associated with large, high-quality videos. Other embodiments are described herein.