Video Transcoding Filter for Machine Learning Model Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning methods are unable to process videos directly, as they are restricted to processing single images and require conversion of videos to images, leading to inefficiencies in storage and resource consumption, and complications in integrating with existing transcoding systems.
Innovation Solution
A method that converts a model file from a machine learning framework into a file identifiable by a transcoding filter, allowing for direct video processing without converting videos to images, thereby reducing storage consumption and decoupling the machine learning process from the transcoding system, enabling efficient video transcoding integrated with machine learning methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning methods are used to process videos by converting them to images, then processing capability is improved, but storage consumption increases and processing efficiency decreases
Solution Approach 1:
The patent introduces an intermediary component (video processing module) that bridges machine learning image processing capabilities and video data without requiring full conversion to images. This module can selectively process video frames or extracted key frames, maintaining the benefits of machine learning while reducing storage consumption by preserving video format for non-processed portions.
Solution Approach 2:
Instead of converting entire videos to images (excessive action), the system applies machine learning processing only to selected frames or regions of interest (partial action). This selective approach reduces the quantity of data that needs to be stored and processed, thereby lowering storage consumption while maintaining processing capability.
2Adaptability or versatility
If machine learning methods are used to process videos by converting them to images, then processing capability is improved, but processing efficiency decreases
Solution Approach 1:
The system applies machine learning processing only to selected frames or regions rather than entire videos, reducing the total processing workload and improving processing efficiency while maintaining the ability to perform complex video analysis where needed.
Solution Approach 2:
The patent implements periodic processing where machine learning is applied at intervals or to key frames rather than continuously to all video data. This periodic approach maintains processing capability for critical analysis while significantly improving overall processing efficiency by reducing the frequency of computationally intensive operations.
3Adaptability or versatility
If machine learning framework is integrated into transcoding system, then processing capability is improved, but system complexity increases
Solution Approach 1:
The patent segments the system into distinct modules: a machine learning processing module and a video transcoding module. This segmentation allows each module to operate independently with well-defined interfaces, reducing system complexity while maintaining enhanced processing capability. The video processing module acts as an intermediary that can be added without fundamentally altering the existing transcoding system architecture.
Data Source
AI summary
The present disclosure provides methods, apparatuses and systems for transcoding a video. One exemplary method for transcoding a video includes: receiving a model file issued by a machine learning framework; converting the model file to a file identifiable by a filter; setting one or more filtering parameters according to the file; and processing the video based on the filtering parameters. According to the embodiments of the present disclosure, no extra storage will be consumed in the process of video transcoding, and system resource consumption can be reduced without adding any extra system deployment costs.