Multimedia Stream Object Removal for Bandwidth Reduction
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Solution Overview
Problem
Current methods for reducing the size of multimedia streams for efficient transmission are inefficient and complex, leading to slower data transmission rates and increased bandwidth usage.
Innovation Solution
A method and system that uses R2 statistical regression and component analysis to identify primary and non-primary focus objects in audio/video streams, allowing for the removal of non-essential objects and the generation of a modified stream that reduces storage and transfer size requirements, while presenting the user with a focused graphical interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If current compression and transmission methods are used to reduce multimedia stream size, then storage and transfer size requirements are reduced, but data transmission rates become slower and systems become more complex
Solution Approach 1:
The patent extracts and removes non-primary focus objects from the multimedia stream while preserving primary focus objects. This selective removal reduces the overall data quantity that needs to be transmitted and stored, directly addressing the contradiction by decreasing stream size without requiring complex compression algorithms that would slow transmission.
Solution Approach 2:
The patent segments the multimedia stream into primary focus objects and non-primary focus objects using R2 statistical regression and component analysis. By dividing the stream into these distinct segments and selectively processing them, the system achieves efficient size reduction while maintaining transmission speed through intelligent object-based processing rather than blanket compression.
2Quantity of substance
If current compression and transmission methods are used to reduce multimedia stream size, then storage and transfer size requirements are reduced, but system complexity increases
Solution Approach 1:
The patent extracts non-primary focus objects for removal from the stream. This extraction approach is simpler than applying complex compression algorithms because it selectively eliminates data based on object importance rather than attempting to compress all data uniformly, thereby reducing stream size with lower system complexity.
Solution Approach 2:
The patent changes the parameter of object importance (primary vs. non-primary focus) to determine which objects to remove. This parameter-based approach simplifies the system by using clear classification criteria rather than complex compression algorithms, achieving size reduction through intelligent selection rather than mathematical transformation.
3Productivity
If R2 statistical regression and component analysis are used to identify and remove non-primary focus objects, then multimedia stream size is reduced and transmission speed improves, but manufacturing and implementation complexity increases
Solution Approach 1:
The patent uses R2 statistical regression to analyze object movements and change parameters such as velocity, acceleration, and positional relationships. Component analysis then identifies primary focus objects based on these parameter changes. This parameter-based approach enables automated, intelligent processing that improves transmission speed while keeping implementation relatively straightforward through mathematical analysis rather than complex hardware modifications.
Solution Approach 2:
The patent implements feedback through continuous analysis of object movements and interactions, using R2 statistical regression to update predictions about which objects are primary focus objects. This feedback mechanism allows the system to adaptively improve transmission efficiency by learning from observed patterns, making implementation easier through software-based adaptation rather than complex hardware design.
Data Source
AI summary
A method and system for improving audio and video multimedia modification and presentation is provided. The method includes receiving an audio/video stream and analyzing objects of the audio/video stream for generating predictions with respect to the objects. Component analysis code is executed with respect to the audio/video stream and an object is removed from the audio/video stream resulting in a modified audio/video stream being generated thereby reducing hardware storage and transfer size requirements of the audio/video stream. The modified audio/video stream is presented to a user via a graphical user interface.