AI Video Category Classification for GOP Encoding Optimization
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Solution Overview
Problem
Existing video compression technologies lack the ability to optimize encoding based on category-specific characteristics, leading to suboptimal compression efficiency and quality in multimedia content delivery.
Innovation Solution
A method and system utilizing artificial intelligence to classify video content into categories, analyze feature elements, and estimate optimal compression options on a Group Of Pictures (GOP) basis, enabling GOP-by-GOP encoding and transcoding without encoding the entire video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional video compression technology is used, then encoding can be performed on the entire video, but compression efficiency and quality are suboptimal because category-specific characteristics are not considered
Solution Approach 1:
The video is divided into multiple categories based on content characteristics (e.g., natural scenes, urban scenes, sports, concerts). Each category is encoded using category-specific compression parameters optimized for its characteristics, rather than applying a single encoding scheme to the entire video. This segmentation enables differentiated compression strategies that improve overall efficiency.
Solution Approach 2:
The system performs preliminary classification of video content into categories before encoding. By analyzing video characteristics in advance and determining the appropriate category, the system can pre-select optimal compression parameters for each category, avoiding the need for complex real-time adjustments during encoding.
2Manufacturing precision
If category-specific optimization is implemented, then compression efficiency improves, but the system complexity increases due to additional classification and parameter selection steps
Solution Approach 1:
The video encoding system automatically performs category classification and selects appropriate compression parameters without requiring manual intervention. The system self-adapts to different video content types by analyzing inherent video characteristics and autonomously determining the optimal encoding approach for each category.
Solution Approach 2:
The system dynamically adjusts compression parameters based on the identified video category. Different categories have different optimal parameter settings (e.g., bitrate, quantization, frame sampling), and the system changes these parameters automatically according to the video content being encoded.
Data Source
AI summary
Proposed are a method for optimizing encoding through video category classification based on artificial intelligence, and a device and a system therefor. A method for optimizing video encoding based on artificial intelligence includes dividing an input video file into groups of pictures (GOPs), performing classification of a category and extraction of feature information on each of the groups of pictures resulting from division, estimating, on the basis of the extracted feature information, a compression option value corresponding to the classified category, performing GOP-by-GOP encoding by applying the compression option value, and combining GOP-by-GOP encoded files to generate an entire video file transcoded.