2D to Stereoscopic Conversion via Local Motion Region Segmentation
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Solution Overview
Problem
Conventional stereoscopic image conversion systems face challenges in accurately separating moving objects from the background and assigning different depths, leading to stereoscopic conversion artifacts due to segmentation errors in depth map generation.
Innovation Solution
A two-dimensional to stereoscopic conversion method that estimates local motion regions, generates a color model based on these regions, calculates similarity values for image pixels, and assigns depth values to generate a stereoscopic image, improving the quality of depth map generation by distinguishing foreground and background pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional depth map generation methods are used, then the process is simple, but segmentation errors occur leading to stereoscopic conversion artifacts
Solution Approach 1:
The patent segments the image into multiple motion regions based on local motion estimation, allowing different depth assignment strategies to be applied to different regions. This segmentation enables more accurate depth map generation by treating foreground and background regions differently, thereby reducing stereoscopic conversion artifacts while managing complexity through region-based processing.
Solution Approach 2:
The patent applies local quality by generating color models specific to each motion region rather than using a global model. Each region's color model is tailored to its local characteristics, improving depth map accuracy for that specific region. This localized approach enhances overall depth map precision without requiring uniformly complex processing across the entire image.
2Measurement precision
If motion-based depth assignment is used, then foreground and background distinction is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary motion estimation and region segmentation before depth map generation. By pre-identifying motion regions and their characteristics, the system prepares the necessary information in advance, allowing the actual depth assignment to proceed more efficiently. This preliminary action reduces the computational burden during the main depth generation phase, thereby reducing processing time while maintaining accurate foreground-background separation.
Solution Approach 2:
The patent changes parameters dynamically based on motion region characteristics. Different motion regions have different color models and depth assignment parameters adapted to their specific properties. This parameter adaptation improves separation accuracy for each region while avoiding the need for uniformly complex processing, thus optimizing the balance between accuracy and processing time.
3Measurement precision
If color model generation is applied, then depth assignment accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent divides the image into motion regions and generates color models only for each region rather than for the entire image. This segmentation reduces the total computational power required while maintaining or improving depth assignment accuracy within each region. The region-based approach focuses computational resources where they are most needed.
Solution Approach 2:
The patent applies color model generation selectively to motion regions rather than uniformly across all pixels. This partial application of the color model reduces overall computational complexity while still achieving improved depth assignment accuracy for the critical motion regions that contribute most to stereoscopic conversion quality.
Data Source
AI summary
In one embodiment, a two-dimensional to stereoscopic conversion method, comprising: estimating a local motion region in a first image relative to one or more second images, the first and the one or more second images comprising two-dimensional images; generating a color model based on the local motion region; calculating a similarity value for each of at least one image pixel selected from the first image based on the color model; and assigning a depth value for each of the at least one image pixel selected from the first image based on the calculated similarity value to generate a stereoscopic image, the method performed by one or more processors.