Video Data Size Reduction via Relevancy-Based Segmentation
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Solution Overview
Problem
Current technologies face challenges in efficiently reducing the size of video data files while maintaining relevance, leading to storage issues and inefficiencies in data management.
Innovation Solution
A method that involves determining the relevancy of sections within video data files and reducing their size accordingly, using predictive models and natural language processing to identify and prioritize content, with options to reduce pixel resolution, remove frames, or freeze frames based on relevancy scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If video data files are stored in full resolution, then content quality is maintained, but storage space consumption increases
Solution Approach 1:
The video data file is divided into multiple sections based on relevancy scores. High-relevancy sections are retained at full quality while low-relevancy sections are reduced in size. This segmentation allows differential quality treatment across different portions of the same data file, resolving the contradiction between maintaining content quality and reducing storage space.
Solution Approach 2:
Different sections of the video data file are assigned different quality levels based on their relevancy scores. Sections with higher relevancy scores maintain full resolution and quality, while sections with lower scores are compressed or reduced. This local quality differentiation optimizes the balance between information preservation and storage efficiency.
2Productivity
If all video sections are processed equally, then processing simplicity is maintained, but processing efficiency decreases
Solution Approach 1:
The processing pipeline segments video data into distinct sections based on relevancy scores before applying different processing operations. This segmentation enables efficient processing by applying appropriate operations to appropriate sections, improving overall processing efficiency while managing complexity through structured differentiation.
Solution Approach 2:
The processing system dynamically adjusts processing intensity based on relevancy scores. High-relevancy sections receive minimal processing or no processing, while low-relevancy sections undergo compression or reduction. This dynamic processing approach optimizes productivity by allocating computational resources based on actual needs rather than treating all sections uniformly.
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: storing into a storage device a video data file; examining video file data defining the video data file, wherein the examining includes determining a relevancy of a section of the video data file; reducing a size of file data defining the section of the video data file in dependence on the determining the relevancy of the section of the video data file; storing a reduced-size version of the video data file into the storage device, the reduced-size version of the video data file having a reduced size relative to the video data file by performance of the reducing the size of file data defining the section of the video data file in dependence on the determining the relevancy of the section of the video data file.