2D to 3D Image Conversion Using K-Means Pixel Segmentation
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Solution Overview
Problem
Existing methods for converting two-dimensional image data into three-dimensional data, such as structure from motion (SFM) and Depth From Cues (DFC), are either complex and require significant processing power or result in poor visual quality due to flicker and uneven depth determination.
Innovation Solution
The method employs a k-means algorithm to segment pixels based on luma, chroma, and motion information, creating pixel clusters and determining depth maps, which are then scaled and adjusted for real-time conversion to reduce processing time and improve visual quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If structure from motion (SFM) approach is used to convert 2D image data to 3D image data, then depth accuracy is improved, but processing time and computational complexity increase significantly
Solution Approach 1:
The patent segments the image into multiple depth layers by analyzing motion vectors of pixel groups. Instead of processing all pixels uniformly to determine depth, the method divides pixels into foreground and background groups based on motion characteristics, then assigns depth values to each group. This segmentation approach maintains depth accuracy while significantly reducing computational complexity and processing time compared to full SFM processing.
Solution Approach 2:
The patent applies partial action by using motion vectors from only certain key pixels or pixel groups rather than all pixels in the image. The method selectively processes pixels that provide sufficient depth information while skipping redundant calculations, thereby achieving acceptable depth accuracy with reduced processing time and computational resources.
2Productivity
If Depth From Cues (DFC) technique is used for real-time conversion, then processing speed is improved, but visual quality deteriorates due to flicker and uneven depth determination
Solution Approach 1:
The patent dynamically adjusts depth determination based on motion detection. By analyzing motion vectors between frames, the system identifies regions with significant motion and applies enhanced depth processing only to those regions, while using simpler methods for static regions. This dynamic approach maintains visual quality in moving areas while preserving processing speed overall, reducing flicker and uneven depth artifacts.
Solution Approach 2:
The patent incorporates feedback mechanisms by using depth information from previous frames to inform current frame processing. The system compares motion patterns and depth consistency across frames, adjusting depth determination in real-time to maintain visual quality. This feedback loop helps eliminate flicker and ensures consistent depth perception across the video sequence.
Data Source
AI summary
Two dimensional data is converted into three dimensional picture data in a method that can provide a real time high quality display during conversion. Pixels of a frame of picture data are segmented to create pixel segments by applying a k-means algorithm. The k-means algorithm groups pixels based on closeness of a combined value that includes luma, chroma, and motion information. By balancing this information the algorithm collects pixels into groups that are assigned relative depths to turn the two-dimensional information into three-dimensional information for display. Another method includes determining a depth map for the different pixel segments by determining an amount of motion of one of the pixel segments between two frames of a video and scaling the three-dimensional depth of one of the pixel segments based on the amount of motion between the two frames.