Presentation Video Frame Extraction for Duplicate-Free Slide Documents
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Solution Overview
Problem
Existing video playback methods for presentations are inefficient and resource-intensive when users seek to locate specific topics of interest, often requiring repeated random jumps and fast-forwarding, leading to wasted time and resource consumption.
Innovation Solution
A system captures unique still images from video frames, excluding duplicates and irrelevant content, and compiles them into a document format for easy scanning, using techniques like image masking, hashing, and neural networks to identify relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users randomly jump to points in the video to locate topics of interest, then they may find the desired content, but they waste time and computing resources through repeated unsuccessful attempts
Solution Approach 1:
The system performs preliminary analysis of the video content before playback, generating a table of contents and extracting key frames in advance. This allows users to quickly navigate to topics of interest without random jumping, as the content structure is pre-processed and organized.
Solution Approach 2:
The patent introduces an intermediary system that acts between the user and the video content. This system includes a content analyzer that extracts semantic information, generates a table of contents, and provides intelligent navigation, mediating the interaction between user queries and video content.
2Speed
If the entire video is buffered to memory for playback, then playback smoothness is improved, but storage and memory resources are wasted when only portions are needed
Solution Approach 1:
The video content is segmented into logical sections based on the generated table of contents, with each section corresponding to a specific topic. The system buffers only the relevant segment when a user requests a particular topic, rather than loading the entire video into memory.
Solution Approach 2:
The video content is pre-processed to generate a table of contents and identify key frames for each section. This preliminary action enables the system to quickly locate and buffer only the necessary portions of the video when users request specific topics.
3Reliability
If processing resources are allocated to decode and buffer video files for playback, then playback quality is maintained, but resources are wasted when users only need specific portions
Solution Approach 1:
The system applies partial processing by decoding and buffering only the specific video segments that are requested, rather than processing the entire video file. The table of contents enables precise identification of which portions need to be processed.
Solution Approach 2:
An intermediary content analysis system processes the video once to extract semantic information and generate a table of contents, which then mediates subsequent playback requests by identifying exactly which segments need to be decoded and buffered.
4Reliability
If users watch the entire video at normal playback to ensure they don't miss the topic of interest, then they guarantee finding the content, but time consumption increases significantly
Solution Approach 1:
The system performs preliminary content analysis to generate a structured table of contents with key frames representing each topic. This allows users to reliably find specific topics by navigating to the relevant section in the table of contents, guaranteeing they won't miss the content without watching the entire video.
Data Source
AI summary
Presentations are often broadcast or video recorded without handouts, such as the slides presented by a presenter of the presentation. Without handouts it is difficult and resource intensive to identify a particular slide of interest. Systems and methods are provided to automatically convert frames of a presentation video (e.g., a file or broadcast) to images in a document (e.g., Word™, PDF, etc.). Frames of the presentation are included in the document unless excluded. The frames are excluded when determined to be duplicative of a previous frame. Frames are duplicative if they are determined to be identical or nearly identical when hashed via an image hashing function. A non-identical frame is further determined to be duplicative when not identical to a previous frame but the differences are limited to only irrelevant visual content (e.g., the presenter gesturing).