Alpha Mask Generation via Downsampled Coefficient Estimation
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Solution Overview
Problem
Current methods for generating alpha masks in video frames are inefficient, making it challenging to display videos in real time due to complex calculations and high computational requirements.
Innovation Solution
A method that involves obtaining a first alpha mask of a video frame, downsampling the frame to a lower resolution, estimating primary and secondary coefficients, and using these coefficients to generate subsequent alpha masks quickly, leveraging convolution operations suitable for graphics processing units (GPUs) to maintain real-time processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex calculations and iterative nonlinear estimation methods are used to generate alpha masks, then measurement precision and manufacturing precision are improved, but productivity is worsened due to high computational requirements and time consumption
Solution Approach 1:
The patent uses a low-resolution copy of the video frame to estimate coefficients, then applies these coefficients to generate high-resolution alpha masks. This copying approach allows rapid coefficient estimation on downsampled data while maintaining high output resolution, resolving the contradiction between precision and speed.
Solution Approach 2:
The patent performs preliminary coefficient estimation using low-resolution frames before generating the final high-resolution alpha masks. By pre-calculating coefficients on simplified data, the system avoids performing complex iterative calculations on full-resolution frames in real-time, thus improving productivity while maintaining precision.
2Measurement precision
If iterative nonlinear estimation methods are used for alpha mask generation, then measurement precision is improved, but loss of time increases due to multiple alternating estimation steps
Solution Approach 1:
The method estimates coefficients on a copied low-resolution version of the frame rather than performing iterative estimation on the original high-resolution frame. This copying strategy dramatically reduces processing time while preserving estimation accuracy through subsequent application to high-resolution data.
Solution Approach 2:
The patent segments the alpha mask generation process into two distinct stages: (1) coefficient estimation on low-resolution frames, and (2) high-resolution alpha mask generation using pre-estimated coefficients. This segmentation eliminates the need for iterative nonlinear estimation on full-resolution frames, reducing time loss while maintaining precision.
3Measurement precision
If high-resolution video frames are processed directly for alpha mask generation, then measurement precision is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent divides the processing task by resolution level: low-resolution processing for coefficient estimation and high-resolution processing for final mask generation. This segmentation reduces device complexity during the computationally intensive coefficient estimation phase while preserving high-resolution detail in the final output.
Solution Approach 2:
By performing coefficient estimation in advance on low-resolution frames, the system reduces the computational complexity of real-time high-resolution processing. The preliminary action on simplified data prepares necessary parameters that simplify subsequent high-resolution mask generation.
Data Source
AI summary
Disclosed is an electronic device and a method to generate alpha masks of video frames in a video comprising a plurality of video frames including a first video frame and a second video frame following the first video frame, where the method comprising; obtaining a first alpha mask of the first video frame; providing a first downscaled video frame, wherein the first downscaled video frame is a lower resolution version of the first video frame; providing a first downscaled alpha mask of the first alpha mask; estimating a first primary coefficient and a first secondary coefficient based on the first downscaled video frame and the first downscaled alpha mask; and generating a second alpha mask for the second video frame based on the first primary coefficient and the first secondary coefficient.