Mono-to-Stereo Image Conversion with Depth Warping for 3D Comfort
Solution Overview
Problem
Existing techniques for viewing two-dimensional images do not adequately enhance realism or provide efficient and desirable viewing experiences.
Innovation Solution
The conversion of mono images to stereo image pairs using viewpoint-based warping, depth-based warping, and boundary adjustment processes, along with comfort-based 3D style presets, to generate high-resolution stereo images that preserve details and adjust for user comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If viewpoint-based warping is used to generate stereo image pairs from mono images, then 3D viewing experience is enhanced, but image resolution and detail quality deteriorate
Solution Approach 1:
The patent performs preliminary actions by generating multiple candidate stereo image pairs with different disparity values before final selection. The system pre-calculates various warping options and evaluates them against quality metrics, then selects the optimal pair that maintains both 3D effect and image quality. This preliminary evaluation prevents loss of resolution by choosing the best possible warping parameters in advance.
Solution Approach 2:
The system dynamically changes warping parameters including disparity values, blending ratios, and coordinate transformation factors to optimize the balance between 3D viewing experience and image quality. By adjusting these parameters based on scene depth information and quality metrics, the system maintains high resolution while enabling effective stereo viewing.
2Manufacturing precision
If depth-based warping is applied to preserve image resolution, then image quality is maintained, but processing complexity increases
Solution Approach 1:
The patent segments the image processing into distinct stages: depth map generation, coordinate transformation, warping operation, and quality evaluation. Each stage handles a specific aspect of the conversion process, allowing for optimized processing at each step and reducing overall complexity while maintaining image quality.
Solution Approach 2:
The system introduces intermediate structures including depth maps, coordinate transformation matrices, and candidate image pairs as mediators between the input mono image and final stereo output. These intermediaries organize the complex processing pipeline and enable quality control at each transformation stage without overwhelming computational complexity.
3Manufacturing precision
If boundary adjustment processes are used to preserve foreground details, then image realism is improved, but processing time increases
Solution Approach 1:
The patent applies local quality enhancement by performing boundary adjustment and detail preservation operations specifically on foreground regions identified through depth analysis. Rather than processing the entire image uniformly, the system focuses computational resources on boundary regions where detail preservation is most critical, reducing overall processing time while maintaining realism.
Solution Approach 2:
The system performs partial action by applying full boundary adjustment only to critical foreground regions while using simplified processing for background areas. This selective approach maintains image realism where it matters most (at object boundaries) while reducing processing time by avoiding exhaustive processing of all image regions.
4Ease of operation
If comfort parameters are adjusted for user preference, then viewing comfort is improved, but system complexity increases
Solution Approach 1:
The patent implements universal comfort adjustment by designing a unified parameter control system that simultaneously manages multiple comfort-related variables including disparity, convergence, and depth scaling. This multi-functional control mechanism allows users to adjust a single set of parameters that affect multiple aspects of viewing comfort, reducing system complexity while improving ease of operation.
Data Source
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AI summary
Various implementations disclosed herein include devices, systems, and methods that convert a mono image to a stereo image pair. For example, a process may obtain an input image including appearance values at pixel positions. The input image corresponds to an appearance of a scene from a first viewpoint. The process may further determine a depth image comprising depth values at original pixel positions that are mapped to corresponding pixel positions of the input image. The process may further generate a first output image corresponding to a second viewpoint of the scene different than the first viewpoint and a second output image corresponding to a third viewpoint of the scene different than the second viewpoint. The process may further enable comfort-based 3D style presets for adjusting comfort parameters for viewing a stereo image pair.