Video Encoding Parameter Prediction via Scene Segmentation
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Solution Overview
Problem
Current video transcoding methods lack precision in adapting to varying video content complexities, leading to suboptimal compression ratios and video quality across different network bandwidths and terminal capabilities.
Innovation Solution
A method and apparatus that divide videos into segments based on scenes, extract features, and use a pre-trained encoding parameter prediction model to determine optimal encoding parameters for each segment, ensuring content-adaptive encoding that improves compression efficiency and video quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If encoding is performed multiple times with adjusted parameters based on overall video complexity, then video quality can be maintained across different network conditions, but the adaptation precision is insufficient because the basis is the entire video level rather than scene-level granularity
Solution Approach 1:
The video is divided into multiple video segments based on scene boundaries detected through scene change detection algorithms. Each segment is then independently encoded with scene-specific parameters rather than applying a single set of parameters to the entire video. This segmentation enables finer-grained content adaptation while maintaining manageable encoding complexity through localized parameter adjustment.
Solution Approach 2:
Different encoding parameters are assigned to different video segments based on their local content characteristics. The system calculates complexity metrics for each segment and applies localized quality adjustments, allowing high-quality encoding for important scenes and lower quality for less critical segments, thereby improving overall adaptation precision without uniformly increasing complexity across the entire video.
2Measurement precision
If scene-based segmentation is implemented to achieve finer content adaptation granularity, then encoding precision improves, but the complexity of the encoding process increases
Solution Approach 1:
Scene change detection and video segmentation are performed in advance before the actual encoding process. Complexity metrics and encoding parameters are pre-calculated for each segment based on content analysis. This preliminary action separates the complex analysis phase from the encoding phase, allowing precise scene-level adaptation to be achieved without proportionally increasing the complexity of the encoding execution itself.
Data Source
AI summary
Embodiments of the present disclosure relate to a method and apparatus for processing a video. The method can include: acquiring a to-be-processed video, and dividing the video into at least one video segment according to a scene; for a video segment of the at least one video segment, extracting a feature of the video segment; inputting the feature and a target video quality into a pre-trained encoding parameter prediction model, to obtain an encoding parameter of the video segment, the encoding parameter prediction model being used to represent a corresponding relationship between the feature of the video segment and the encoding parameter under the target video quality; and performing an encoding process on the video according to the encoding parameter corresponding to the video segment of the at least one video segment, to generate an encoded video.