Block-Based Occlusion Detection in Video Frames
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Solution Overview
Problem
Conventional occlusion detection methods in image processing face challenges in accuracy and computational complexity, particularly in identifying occlusion areas between consecutive frames, which can lead to artifacts in temporal interpolation.
Innovation Solution
A block-based method that determines occlusion areas by calculating block differences and predicted block differences using bidirectional motion vectors, where blocks are identified as occlusion areas if the difference exceeds a threshold, reducing computational complexity and improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pixel difference-based occlusion detection is used, then occlusion areas can be detected, but computational complexity increases and detection accuracy decreases
Solution Approach 1:
The patent divides the image into multiple blocks and processes each block independently to determine occlusion areas. This segmentation approach reduces computational complexity by avoiding pixel-level processing across the entire image while maintaining detection accuracy through localized analysis of block differences and predicted block differences.
Solution Approach 2:
The patent applies different processing strategies to different blocks based on their characteristics. By analyzing local block differences and comparing them with predicted block differences, the method adapts its detection approach to local image regions, improving overall detection accuracy without uniformly increasing computational complexity across the entire image.
2Measurement precision
If bidirectional motion vectors are used for block-based analysis, then occlusion detection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent uses bidirectional motion vectors to estimate motion in both forward and backward directions, which provides more comprehensive motion information. This partial excessive action (using more motion vector data than strictly necessary) improves detection accuracy by capturing occlusion areas more reliably, while the block-based framework keeps the overall computational burden manageable.
Data Source
AI summary
A method and apparatus for occlusion area detection based on block difference associated with a motion vector and a predicted block difference are disclosed. For each current block of a frame, motion estimation is performed based on a temporally previous frame and a temporally subsequent frame. Based on the motion vector derived, two reference blocks of the current block are located in the temporally neighboring frames. The block difference between these two reference blocks is calculated for the current block. By comparing the block difference with a predicted block difference of the current block, the current block is determined to be an occlusion block or not accordingly. The predicted block difference is updated by averaging the block difference of neighboring blocks in a non-motion boundary area.