2D to 3D Video Conversion via Segmented Depth Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing real-time 2D to 3D video conversion techniques face high computational complexity due to semantic video content analysis and color-based segmentation, making them unsuitable for real-time processing, especially for HD content.
Innovation Solution
The method involves downscaling 2D video frames to lower resolution, segmenting background and foreground objects, generating separate depth maps for each, and deriving a frame depth map in original resolution using background tracking and structure analysis, thereby reducing computational complexity and enhancing processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If semantic video content analysis and color-based segmentation are used to reconstruct 3D objects, then 3D conversion quality is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the video frame into background and foreground regions, applying different depth estimation methods to each. The background uses geometric depth map generation while the foreground uses color-based segmentation, allowing each region to be processed with the most efficient method for its characteristics, thereby reducing overall computational complexity while maintaining quality.
Solution Approach 2:
The patent applies different depth estimation techniques to different regions of the image based on their characteristics. The background region uses geometric depth mapping while the foreground region uses color-based segmentation, optimizing the quality-complexity tradeoff for each local area rather than applying a uniform method across the entire image.
2Measurement precision
If geometric depth map and color-based segmentation are fused to generate final depth map, then depth information accuracy is improved, but processing time increases
Solution Approach 1:
The patent divides the image processing task into segmented regions (background and foreground) and applies appropriate depth estimation methods to each, reducing the overall processing time while maintaining accuracy through region-specific optimization rather than uniform processing of the entire image.
Solution Approach 2:
The patent applies color-based segmentation only to the foreground region where it is most needed for accuracy, while using faster geometric depth mapping for the background, thereby achieving sufficient accuracy without the excessive processing time that would result from applying both methods uniformly across the entire image.
3Manufacturing precision
If 2D video frames are processed at original resolution, then output quality is maintained, but computational load increases
Solution Approach 1:
The patent segments the processing into two stages: first downsampling the entire frame to a lower resolution for efficient geometric depth map generation, then applying color-based segmentation only to the foreground region at this reduced resolution, and finally upsampling the result to original resolution, thereby maintaining quality while significantly reducing computational load.
Solution Approach 2:
The patent performs preliminary downsampling of the video frame to a lower resolution before applying computationally intensive processing operations, and only upsamples the final depth map to original resolution, thereby reducing the computational load during the main processing stages while maintaining output quality.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method for real-time 2D to 3D video conversion includes receiving a decoded 2D video frame having an original resolution, downscaling the decoded 2D video frame into an associated 2D video frame having a lower resolution, and segmenting objects present in the downscaled 2D video frame into background objects and foreground objects. The method also includes generating a background depth map and a foreground depth map for the downscaled 2D video frame based on the segmented background and foreground objects, and deriving a frame depth map in the original resolution based on the background depth map and the foreground depth map. The method further includes providing a 3D video frame for display at a real-time playback rate. The 3D video frame is generated in the original resolution based on the frame depth map.