Optical Mouse Motion Estimation Using Iterative Candidate Vector Selection
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Solution Overview
Problem
Existing motion estimation methods for optical mice are complex, resource-intensive, and prone to divergence during abrupt motion, requiring efficient generation and selection of candidate vectors to accurately represent mouse movement.
Innovation Solution
A method involving the generation of candidate vectors by adding search vectors to a reference motion vector, with a selection rule based on correlation calculations between pixel blocks, repeated iteratively to improve vector accuracy and reduce computational cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex image processing methods are used to detect mouse motion, then measurement precision is improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent extracts only the essential information needed for motion detection by using a simplified block-matching approach instead of processing entire images. It focuses on comparing specific blocks of pixels between frames rather than analyzing all image data, thereby reducing computational complexity while maintaining adequate measurement precision for cursor control applications.
Solution Approach 2:
The patent segments the image processing task into discrete blocks that are compared between frames. By dividing the processing into smaller block-matching operations rather than full-image analysis, it reduces the overall computational burden while still capturing sufficient motion information for accurate cursor tracking.
2Measurement precision
If complex image processing methods are used to detect mouse motion, then measurement precision is improved, but energy consumption increases
Solution Approach 1:
The patent extracts only the essential information needed for motion detection by using a simplified block-matching approach instead of processing entire images. It focuses on comparing specific blocks of pixels between frames rather than analyzing all image data, thereby reducing computational complexity while maintaining adequate measurement precision for cursor control applications.
Solution Approach 2:
The patent applies partial action by performing motion estimation on selected blocks rather than exhaustive full-image processing. The block-matching algorithm processes only the necessary portions of the image data required to determine cursor motion, avoiding unnecessary computational energy expenditure while achieving sufficient detection accuracy.
3Productivity
If motion estimation is performed quickly to meet time constraints, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent applies partial action by performing motion estimation on selected blocks rather than exhaustive full-image processing. The block-matching algorithm processes only the necessary portions of the image data required to determine cursor motion, avoiding unnecessary computational energy expenditure while achieving sufficient detection accuracy.
Solution Approach 2:
The patent uses preliminary action by utilizing motion vectors from previously processed frames to guide the current frame's motion estimation. This temporal redundancy exploitation allows the system to start with educated guesses about motion direction and magnitude, reducing the search space and computational effort needed while maintaining accuracy even under tight time constraints.
4Measurement precision
If candidate vectors are generated and selected through correlation calculations, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the image processing task into discrete blocks that are compared between frames. By dividing the processing into smaller block-matching operations rather than full-image analysis, it reduces the overall computational burden while still capturing sufficient motion information for accurate cursor tracking.
Solution Approach 2:
The patent extracts only the essential information needed for motion detection by using a simplified block-matching approach instead of processing entire images. It focuses on comparing specific blocks of pixels between frames rather than analyzing all image data, thereby reducing computational complexity while maintaining adequate measurement precision for cursor control applications.
Data Source
AI summary
An image sequence sensor senses images. To associate a motion vector with an image of the sequence currently being processed, k candidate vectors are generated by adding, to a reference motion vector, respectively k search vectors. Then, a motion vector is selected from among the k candidate vectors as a function of a selection rule. Thereafter, the previous two steps are repeated m times, the reference motion vector being on the one hand, for a first iteration of the first step, an initial reference vector selected from among a set of vectors comprising at least one motion vector associated with a previous processed image and being on the other hand, for the m repetitions of the first step, the motion vector selected in the second step preceding the first step. Then, the vector obtained in the third step is associated with the image currently being processed.