Digital Image Stabilization via Bit-Plane Matching
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Solution Overview
Problem
Existing motion estimation techniques for video stabilization in hand-held cameras are either expensive due to the use of gyroscopic sensors or complex, making them unsuitable for devices with limited computational power and memory, such as mobile phones.
Innovation Solution
The proposed digital image stabilization method uses bit-plane matching techniques to estimate camera motion by computing a desired component of the camera motion, excluding undesired jitter, through methods like Sign Projection and Binary Incrementation, which are fast, simple, and reduce numerical complexity, eliminating the need for extra hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gyroscopic sensors are used for electronic image stabilization, then motion detection accuracy is improved, but device cost and complexity increase
Solution Approach 1:
The patent replaces the mechanical gyroscopic sensor system with a digital image processing system that uses bit-plane matching algorithms. Instead of using physical sensors to detect motion, the system processes image data through binary representation and correlation operations to estimate motion vectors, thereby eliminating mechanical components while maintaining motion detection capability
Solution Approach 2:
The patent creates a simplified digital model of motion detection by using bit-plane representations of images. Rather than directly measuring physical motion with sensors, the system creates binary copies of image data at different bit-planes and uses correlation between these copies to infer motion, providing an accurate but simpler alternative to gyroscopic measurement
2Measurement precision
If traditional motion estimation algorithms are used, then motion estimation accuracy is improved, but computational complexity and power consumption increase
Solution Approach 1:
The patent segments the image data into multiple bit-planes, processing each plane separately through binary correlation operations. This segmentation allows the complex motion estimation problem to be broken down into simpler, parallelizable operations that consume less computational power while maintaining overall estimation accuracy
Solution Approach 2:
The patent changes the parameter representation from full grayscale values to binary bit-plane values. By transforming the image data into binary form at different bit-planes and performing correlation operations on these simplified representations, the system reduces computational complexity and power consumption while preserving essential motion information
3Device complexity
If bit-plane matching techniques are used, then computational complexity is reduced, but implementation precision may deteriorate
Solution Approach 1:
The patent adds the dimension of bit-plane decomposition to the motion estimation process. Instead of working with single grayscale values, the system processes multiple binary planes (from least significant to most significant bits), effectively adding a dimensional layer to the computation that preserves precision while simplifying individual operations
Solution Approach 2:
The patent combines multiple bit-plane representations into a composite motion estimation result. By integrating information from different bit-planes through correlation operations and combining the results, the system achieves accurate motion estimation that leverages the strengths of each individual bit-plane while compensating for limitations of any single plane
Data Source
AI summary
An estimated total camera motion between temporally proximate image frames is computed. A desired component of the estimated total camera motion is determined including distinguishing an undesired component of the estimated total camera motion, and including characterizing vector values of motion between the image frames. A counter is incremented for each pixel group having a summed luminance that is greater than a threshold. A counter may be decremented for pixels that are under a second threshold, or a zero bit may be applied to pixels below a single threshold. The threshold or thresholds is/are determined based on a dynamic luminance range of the sequence. The desired camera motion is computed including representing the vector values based on final values of counts for the image frames. A corrected image sequence is generated including the desired component of the estimated total camera motion, and excluding the undesired component.