Video Shakiness Detection via Motion Vector Transformation
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Solution Overview
Problem
Existing video editing technologies are resource-intensive and inefficient in determining and removing shaky portions from video content, as they require significant time, memory, and processing power to assess image sensor movements.
Innovation Solution
A system that determines motion vectors and transformation matrices for video frames to characterize rigid transformations, calculates shakiness metrics based on these matrices, and identifies shaky frames using a shakiness threshold, allowing for efficient editing and generation of a video summary by processing video content stored on physical storage media with processors configured by machine-readable instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to determine shakiness of video content, then measurement precision is improved, but use of energy and processing resources worsens
Solution Approach 1:
The patent segments the video content into individual video frames and processes them independently. The shakiness determination is broken down into frame-level operations where motion vectors are calculated for each frame relative to reference frames, allowing parallel processing and reducing overall computational burden while maintaining measurement precision.
Solution Approach 2:
The patent applies partial action by selectively processing only certain frames (e.g., keyframes or frames with significant motion) rather than analyzing every frame in detail. Motion estimation is performed on a subset of frames using techniques like block matching or optical flow, reducing computational resources while preserving essential shakiness information.
2Measurement precision
If traditional methods are used to determine shakiness of video content, then measurement precision is improved, but loss of time worsens
Solution Approach 1:
The patent performs preliminary action by pre-processing video frames to extract motion vectors and transformation matrices before the actual shakiness determination. Motion estimation is conducted in advance using efficient algorithms, and results are cached for subsequent shakiness analysis, significantly reducing the time required for the main processing task.
Solution Approach 2:
The patent implements skipping by rapidly processing frames that are determined to be stable or redundant. Once a sequence of frames is identified as having minimal motion or being similar to previously analyzed frames, the system skips detailed analysis and proceeds to the next potentially shaky segment, reducing overall processing time while maintaining precision for critical frames.
3Measurement precision
If detailed shakiness analysis is performed on all video frames, then measurement precision is improved, but device complexity worsens
Solution Approach 1:
The patent introduces intermediary elements such as transformation matrices and motion vectors that simplify the shakiness analysis process. These intermediaries act as mediators between the raw video data and the final shakiness determination, breaking down the complex analysis into manageable steps that can be performed with simpler computational operations.
Solution Approach 2:
The system performs self-service by automatically determining shakiness metrics without requiring manual intervention or complex external processing. The video frames themselves provide the necessary information through motion estimation algorithms, and the system self-regulates by adapting the analysis depth based on detected motion patterns, reducing the need for complex external control mechanisms.
Data Source
AI summary
Video information defining video content may be accessed. The video content may include video frames. Motion vectors for the video frames may be determined. The motion vectors may represent motion of one or more visuals captured within individual video frames. A transformation matrix for the video frames may be determined based on the motion vectors. The transformation matrix may characterize rigid transformations between pairs of the video frames. Shakiness metrics for the video frames may be determined based on the transformation matrix. A shakiness threshold may be obtained. One or more of the video frames may be identified based on the shakiness metrics, the shakiness threshold, and algorithms with hysteresis or finite-state machines. A video summary of the video content may be generated. The video summary may include the one or more identified video frames.