Convex Hull Video Encoding for Faster Low-Complexity Compression
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Solution Overview
Problem
Conventional video encoding methods face high computational demands and inefficiencies, leading to increased coding time and reduced coding speed, which strain video processing infrastructure.
Innovation Solution
A convex hull encoding method that splits video clips into shots with similar scenes, applies down-sampling and up-sampling, calculates quality metrics, selects convex hull points, and predicts quantizer parameters to optimize encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional encoding methods are used to improve coding efficiency, then coding quality is improved, but coding speed becomes slower and computational loading increases
Solution Approach 1:
The video clip is divided into multiple video shots based on scene changes. Each shot is independently encoded, allowing parallel processing and reducing overall computational time. This segmentation enables the system to maintain high coding efficiency for each shot while improving overall coding speed through concurrent operations.
Solution Approach 2:
The method performs preliminary analysis of the video clip to identify scene changes and split shots before the actual encoding process. This preliminary action prepares the data structure and parameters needed for efficient encoding, reducing the computational burden during the main encoding phase and improving both speed and resource utilization.
2Manufacturing precision
If complex encoding methods are used to obtain good coding efficiency, then quality is improved, but computational loading to the computing device increases
Solution Approach 1:
Different encoding parameters and quality settings are applied to different video shots based on their specific characteristics. Complex encoding methods are selectively applied only where needed rather than uniformly across the entire video, reducing overall computational loading while maintaining coding efficiency in critical segments.
Solution Approach 2:
The method applies full-complexity encoding only to selected video shots that require it, while using simplified encoding for other shots. This partial application of complex encoding methods reduces computational loading on the computing device while maintaining adequate coding efficiency for the overall video.
Data Source
AI summary
A method of forming a convex hull encoding method is provided. The method includes the step of providing a source video clip; splitting the source video clip by a video splitter with a similar scene; slicing each video shots to a preset length video shots; providing down-sampling process, encoding process, decoding process and up-sampling process to obtain the analysis video shots; calculating a quality index between the video shots and the analysis video shots to obtain quality metrics; selecting convex hull points and calculating slopes between the convex hull points; picking operation points with similar slopes to form operation point series and predicting quantizer parameters; encoding the source video clip by the quantization parameters and the corresponding resolutions to obtain a compressed video.