Video Compression of Crossfade Frames Using Weighted Inter-Prediction
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Solution Overview
Problem
Current methods for determining how to compress video frames consume significant central processing unit (CPU) resources by calculating data sizes of different encoded frame types, which is inefficient and resource-intensive.
Innovation Solution
Dynamically encode crossfade frames as inter-predicted frames using weighted motion vectors based on boundary frames, bypassing the need to estimate data sizes, thereby conserving CPU resources and improving video quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the encoder calculates data sizes of frames as different types of encoded frames to determine the smallest data size, then the video compression efficiency is improved, but the CPU resources (time or power) are significantly consumed
Solution Approach 1:
The system performs preliminary detection of crossfade frames before the main encoding process. By identifying crossfade frames in advance using a scene change detector, the encoder can apply a predetermined encoding strategy (inter-prediction with weighting factors) without needing to calculate and compare data sizes for different frame types, thus saving CPU resources while maintaining compression efficiency
Solution Approach 2:
The system dynamically adjusts the encoding approach based on the detected frame type. For regular frames, the encoder calculates data sizes to determine optimal encoding; for detected crossfade frames, it applies a specialized inter-prediction method with weighting factors. This dynamic adaptation resolves the contradiction by optimizing the balance between compression efficiency and CPU usage based on content characteristics
2Ease of manufacture
If crossfade frames are encoded using traditional methods, then the encoding process is simple, but the video quality and compression efficiency are suboptimal
Solution Approach 1:
The system applies different encoding qualities to different frame types. Crossfade frames detected by the scene change detector receive a specialized encoding treatment using inter-prediction with weighting factors that accounts for the transition nature, while regular frames use traditional encoding. This local differentiation improves video quality for crossfade frames without significantly complicating the overall encoding process
Data Source
AI summary
Systems, methods, and apparatuses are described for compressing digital content. The digital content may comprise a plurality of frames. The plurality of frames may comprise a plurality of crossfade frames. A first boundary frame of the crossfade frames may be determined. A second boundary frame of the crossfade frames may be determined. At least a portion of the crossfade frames may be coded as inter-predicted frames using a weighting factor and based on the first boundary frame or the second boundary frame.