Depth of Field Generation for Naked-Eye 3D Displays
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for generating depth of field from a two-dimensional image in naked-eye 3D displays suffer from image distortion and instability, particularly when converting single viewpoint content into multiple viewpoints, due to issues like image cracking and misalignment.
Innovation Solution
A method that involves classifying a target image into foreground, middleground, and background image classes based on positional and morphological relationships, estimating sharpness values, and using depth value and depth of field value estimation algorithms to generate accurate depth of field, reducing distortion and improving image stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image shifting and continuous image prediction methods are used to obtain single viewpoint depth of field, then depth of field can be generated from two-dimensional images, but image distortion and instability occur including image cracking and misalignment
Solution Approach 1:
The patent segments the two-dimensional image into multiple image classes (foreground, middleground, background) based on depth information. By classifying pixels into different depth layers, the method processes each segment with appropriate depth estimation, reducing cross-layer distortion and improving overall image stability while maintaining depth of field generation accuracy.
Solution Approach 2:
The patent applies different processing strategies to different regions of the image based on their depth characteristics. Foreground regions receive different depth estimation treatment compared to background regions, allowing local optimization of depth of field generation while minimizing artifacts like image cracking and misalignment in specific areas.
2Adaptability or versatility
If single viewpoint contents are converted into multiple viewpoints contents for naked-eye 3D display, then the display capability is improved, but image distortion and instability increase due to depth of field generation issues
Solution Approach 1:
The patent transitions from two-dimensional image data to three-dimensional depth information by estimating depth values for each pixel. This dimensional transformation enables the generation of multiple viewpoints from single viewpoint content while maintaining image quality, as the depth information provides the necessary spatial context for accurate 3D reconstruction without introducing distortion.
3Extent of automation
If depth of field is generated using prior art methods, then conversion from two-dimensional to three-dimensional display is achieved, but image cracking and misalignment artifacts are produced
Solution Approach 1:
The patent incorporates feedback mechanisms in the depth estimation process by using sharpness values and depth ordering constraints to guide the classification and processing of image classes. This feedback loop ensures that depth information is consistently applied across the image, preventing artifacts like image cracking and misalignment while maintaining automated operation.
Data Source
Figure 1~2
Figure 3
Figure 4
AI summary
Disclosed are a two-dimensional image depth of field generation method and device, the method including: obtaining a target image, classifying the target image to obtain corresponding multiple image classes, and calculating the maximum sharpness value corresponding to each image class; respectively acquiring an object of each image class to estimate sharpness value of the object of each image class, and classifying the object of each image class based on the maximum sharpness values corresponding to each image class; estimating depth value of the object of each image class using a depth value estimation algorithm, based on the sharpness value of the classified object of each image class; and estimating depth of field value of the object of each image class using a depth of field value estimation algorithm, based on the depth value of the object of each image class. The present disclosure solves the problem in the prior art that image distortion occurs when obtaining single viewpoint depth of field, and realizes depth of field generation from a two-dimensional image, in order to reduce image distortion and improve image stability, thereby satisfying the demand of users.