Motion Estimation System Using Spatial Propagation for Image Sequences
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Solution Overview
Problem
Imaging apparatuses struggle to produce high-quality images from low-quality image sequences, even with the capability to process high-quality images, due to inefficiencies in motion estimation and image processing.
Innovation Solution
A motion estimation system and method that generates initial and random motion fields by comparing similarity function values, and then propagates motion vectors spatially and temporally to create optimum motion fields, enabling accurate and quick estimation of image motion and smooth motion changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional motion estimation methods are used, then processing speed may be maintained, but motion estimation accuracy and image quality deteriorate
Solution Approach 1:
The image is divided into multiple blocks, and motion vectors are estimated for each block independently. The motion field is segmented into different regions (e.g., foreground and background) with different motion characteristics, allowing accurate motion estimation for each segment while maintaining overall processing efficiency
Solution Approach 2:
Initial motion vectors are predicted using previous frame information and temporal interpolation before final refinement. This preliminary action provides a good starting point for iterative optimization, reducing the number of iterations needed and improving both accuracy and speed
2Manufacturing precision
If low-quality image sequences are processed, then input data is available, but output image quality deteriorates
Solution Approach 1:
Virtual frames are generated by copying and interpolating motion vectors from reference frames. These virtual frames serve as intermediate representations that preserve motion information while enabling high-quality output from low-quality input sequences
Solution Approach 2:
The system changes processing parameters dynamically based on input quality. For low-quality inputs, it adjusts block sizes, motion search ranges, and interpolation factors to optimize the balance between preserving available information and producing high-quality output
3Measurement precision
If complex motion estimation algorithms are used, then motion estimation accuracy improves, but processing time increases
Solution Approach 1:
Motion estimation is performed periodically at key frames with full accuracy, while intermediate frames use lighter estimation methods. This periodic application of complex algorithms maintains accuracy for important frames while reducing overall processing time
Solution Approach 2:
The algorithm dynamically adjusts its complexity based on scene content. For high-motion or complex-scene frames, more accurate but computationally intensive methods are applied. For simple scenes, lighter methods suffice, optimizing the trade-off between accuracy and processing time
Data Source
AI summary
A motion estimation system comprises: an initializing module generating a first initial motion field by allocating an initial motion vector to each block of a first motion field related to a first motion change of an image which accompanies a change from an (n−1)-th frame to an n-th frame and generating a second initial motion field allocating an initial motion vector to each block of a second motion field related to a second motion change of the image which accompanies a change from the n-th frame to the (n−1)-th frame; and a candidate test module generating first and second random motion fields based on a similarity function and each block of each of the first and second initial motion fields and random motion vectors, generating first and second spatial propagation motion fields based on the similarity function, and generating first and second optimum motion fields based on the similarity function.