Background Frame Completion via Pixel Offset Alignment
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Solution Overview
Problem
Current video editing techniques are limited by a two-dimensional workspace, which prevents effective capture and completion of background data when foreground objects obscure the background, leading to noticeable and low-quality fill methods.
Innovation Solution
A method that analyzes a collection of frames to determine spatial offsets and pixel values, identifies foreground and background objects, and completes the background frame by retaining predominant pixel values, using camera motion analysis and vector calculations to distinguish between objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional two-dimensional workspace techniques are used to fill obscured background areas, then the editing process is simple, but the quality of the filled background is poor and noticeable
Solution Approach 1:
The patent transitions from two-dimensional frame analysis to three-dimensional spatiotemporal analysis by incorporating temporal dimension. Multiple frames are analyzed in sequence, and pixel data is aggregated across time to reconstruct obscured background regions, transforming the problem from spatial filling to spatiotemporal synthesis.
Solution Approach 2:
The system performs preliminary analysis of multiple frames before completing the background. By pre-processing frames to identify static background regions and track pixel movements across sequences, the system prepares background models in advance that can be used to fill obscured areas with high quality.
2Manufacturing precision
If pixel data is collected from multiple frames to complete the background, then the background quality improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the image into foreground and background regions using motion detection and pixel tracking. By identifying static pixels that represent background across multiple frames and separating them from moving foreground objects, the system processes only relevant background data, reducing overall computational burden.
Solution Approach 2:
The system analyzes more frames than the minimum required to achieve quality background completion. By collecting pixel data from an excessive number of frames beyond what is strictly necessary, the system ensures high-quality background reconstruction while distributing computational load across more data points.
3Measurement precision
If frame alignment and spatial offset analysis are performed to track pixels, then accurate background completion is achieved, but the device complexity and computational requirements increase
Solution Approach 1:
The patent introduces spatial offset calculations as an intermediary mechanism between frame alignment and pixel tracking. By computing relative displacements between frames and using these offsets to guide pixel correspondence, the system achieves accurate tracking without requiring complex direct matching algorithms.
Data Source
AI summary
Background frames can be completed from a collection of frames having foreground objects that are partially obscuring the pixels comprising the background. The special offset of a pixel represented across a collection of frames can be determined based on camera movement data. By determining the relative offset of a pixel represented in a first frame from the same pixel in other frames, pixel values representing a background object can be accumulated to derive a completed background frame.