Video Data Processing via Salient Feature Segmentation
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Solution Overview
Problem
Conventional video processing systems face inefficiencies in data communication speeds, storage requirements, and perceptual artifacts due to the variability and unconstrained nature of video data, leading to loss of salient information and reduced precision in representation.
Innovation Solution
The integration of linear decompositional, spatial segmentation, and spatial normalization methods to efficiently process video data, focusing on salient component identification and extraction, which reduces computational processing, transmission bandwidth, and storage needs while prioritizing important signal parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional video processing systems process unconstrained video data, then they can handle diverse video content, but data communication speeds decrease and storage requirements increase
Solution Approach 1:
The patent segments video data into distinct components: salient features (edges, corners, contours) and non-salient background information. By separating and independently processing these segments, the system achieves both adaptability to diverse content and improved communication speed through selective transmission of only the most important visual information.
Solution Approach 2:
The patent extracts salient features from video frames using operators like Canny edge detection and Harris corner detection. This extraction isolates the most perceptually important elements, allowing the system to handle diverse video content adaptively while reducing data volume for faster communication and storage.
2Loss of information
If conventional video processing systems process unconstrained video data, then they can maintain comprehensive information, but storage requirements increase
Solution Approach 1:
The patent extracts and preserves only salient features (edges, corners, contours) while discarding redundant background information. This selective extraction maintains the most important visual information for accurate video representation while dramatically reducing storage requirements by eliminating unnecessary data.
Solution Approach 2:
The patent applies different processing quality levels to different parts of the video data. Salient features receive high-quality preservation with detailed representation, while non-salient background areas use compressed or simplified representation, optimizing the balance between information preservation and storage efficiency.
3Measurement precision
If linear decompositional methods are applied to unconstrained video data, then computational processing is intensive, but representation accuracy can be achieved
Solution Approach 1:
The patent segments video data into salient features and background before applying linear decompositional methods. This segmentation reduces the dimensionality of the data requiring intensive computational processing, thereby improving productivity while maintaining representation precision for the most important visual elements.
Solution Approach 2:
The patent performs preliminary feature extraction (edges, corners, contours) before applying computationally intensive linear decompositional methods. This preliminary action pre-processes the data to identify and isolate critical information, reducing the computational burden of subsequent processing while preserving representation accuracy.
4Productivity
If spatial segmentation and normalization are applied, then processing efficiency increases, but device complexity increases
Solution Approach 1:
The patent implements spatial segmentation to divide video frames into regions of interest and background areas, then applies normalization selectively. This segmentation approach improves processing efficiency by focusing computational resources on salient regions while using simpler processing for background areas, managing device complexity through differentiated processing strategies.
Solution Approach 2:
The patent applies spatial normalization with different parameters and intensities to different spatial regions. Salient features receive precise normalization to enhance processing efficiency, while background areas use simpler normalization, balancing productivity gains with controlled device complexity.
Data Source
AI summary
An apparatus and methods for processing video data are described. The invention provides a representation of video data that can be used to assess agreement between the data and a fitting model for a particular parameterization of the data. This allows the comparison of different parameterization techniques and the selection of the optimum one for continued video processing of the particular data. The representation can be utilized in intermediate form as part of a larger process or as a feedback mechanism for processing video data. When utilized in its intermediate form, the invention can be used in processes for storage, enhancement, refinement, feature extraction, compression, coding, and transmission of video data. The invention serves to extract salient information in a robust and efficient manner while addressing the problems typically associated with video data sources.