Intelligent Video Thumbnail Selection via Relevancy Metrics
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Solution Overview
Problem
High-traffic video sharing websites face challenges in quickly generating thumbnail images that are both visually stimulating and representative of video content, as existing automated processes often fail to ensure thumbnails are appealing and accurately depict the video, leading to reduced viewer engagement and increased costs for manual selection and modification.
Innovation Solution
A system and method for selecting and generating thumbnail images by computing relevancy metrics based on content features, such as visual stimulation and relevance, to identify frames that are clear, high-contrast, and representative of the video content, which can include pre-processing video files, selecting shots, and post-processing frames for enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated thumbnail generation processes are used, then productivity is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system computes a relevancy metric for each frame based on multiple content features (visual stimulation, object detection, text presence, etc.) and uses this feedback to automatically select the most representative frame. This closed-loop approach ensures that automated selection achieves high precision by continuously evaluating and comparing frames against established criteria.
Solution Approach 2:
The patent transforms the thumbnail selection problem into a parameter-based optimization problem by defining quantifiable metrics for visual stimulation, object presence, text content, and other features. By changing the selection criterion from random or manual choice to parameter-driven optimization, the system achieves both high speed and high precision simultaneously.
2Manufacturing precision
If manual thumbnail selection is used, then manufacturing precision is improved, but productivity deteriorates
Solution Approach 1:
The system enables thumbnails to select themselves automatically by implementing an algorithm that autonomously evaluates all frames using predefined relevancy criteria. The computer automatically identifies and selects the most representative frame without human intervention, achieving both high precision and high speed through self-service automation.
3Productivity
If simple automated selection is used, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments the thumbnail selection process into multiple independent evaluation dimensions: visual stimulation metrics, object detection results, text presence analysis, color distribution, and composition quality. By dividing the assessment into these discrete segments, the system can evaluate each aspect with high precision and combine them to determine overall relevancy, achieving both speed and accuracy.
Data Source
AI summary
In accordance with one embodiment, an intelligent video thumbnail selection and generation tool may select a relevant and visually stimulating image from a video file and generate a thumbnail including the image. The image may be selected by computing a relevancy metric for an image in the file based on one or more selected relevant features, and comparing that relevancy metric with the metric of at least one other image in the file. In another embodiment, a series of images in a video file may be divided into shots. One of the shots may be selected based on a shot relevancy metric and a key image from the shot may be selected as a thumbnail based on a key image relevancy metric, where the shot relevancy metric and the key image relevancy metrics may be computed based on one or more relevant content features.