Image Processing Apparatus for Depth Image-Based Rendering Hole Restoration
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Solution Overview
Problem
Existing image processing methods for depth image-based rendering (DIBR) struggle to accurately restore hole regions in color images acquired from different viewpoints, leading to degradation in image clarity due to differences in pixel values between hole regions and their neighboring areas.
Innovation Solution
An image processing apparatus that estimates the motion of objects within images, determines neighboring images based on this motion, and calculates pixel values for hole regions using neighboring images, dividing the hole region into sub-regions if necessary, to enhance the accuracy of pixel value determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a hole region is restored by determining a pixel value of the hole region based on a pixel value of a neighboring region of the hole region, then the hole region can be restored, but the restored hole region differs from the original hole region due to pixel value differences, resulting in degradation in image clarity
Solution Approach 1:
The hole region is divided into multiple sub-regions based on depth information and motion characteristics. Each sub-region is restored using appropriate pixel values from different temporal references (current frame, previous frame, or next frame), allowing differentiated restoration strategies for different parts of the hole region to improve overall accuracy and clarity
Solution Approach 2:
Motion estimation is performed in advance to predict the motion of objects and determine which temporal reference (current, previous, or next frame) should be used for restoring each hole region sub-region. This preliminary motion analysis enables the system to select the most appropriate pixel values before actual restoration, improving both accuracy and clarity
2Adaptability or versatility
If depth image based rendering is used to generate color images from different viewpoints, then three-dimensional imaging capability is achieved, but hole regions appear due to occlusion that cannot be properly restored
Solution Approach 1:
Motion estimation and depth information serve as intermediaries between the occluded hole regions and the pixel values from temporal references. By using motion vectors to track object movement and depth information to identify occlusion boundaries, the system can accurately map pixel values from appropriate temporal frames to restore hole regions, maintaining both 3D capability and restoration accuracy
Solution Approach 2:
The restoration approach dynamically adapts based on motion characteristics. The system determines whether to use previous frame, next frame, or current frame pixel values based on motion estimation results. This dynamic selection allows the system to handle different motion scenarios (fast motion, slow motion, stationary objects) effectively, maintaining hole region restoration accuracy across varying conditions
Data Source
AI summary
An apparatus and method for processing an image based on a motion of an object, the apparatus including a motion estimator configured to estimate a motion of an object included in a current image, an image determiner configured to determine a neighboring image neighboring the current image based on the motion of the object, and a pixel value determiner configured to determine a pixel value of a hole region neighboring the object based on the neighboring image is provided.