Spatio-Temporal Integration for Compressive Video Measurement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional compressive imaging methods are inefficient for capturing video data, particularly when dealing with large amounts of raw data, as they perform only spatial projection/integration, losing information between snapshots or requiring extensive data capture for fast actions, and are computationally demanding.
Innovation Solution
An apparatus that generates compressive measurements of video using spatial-temporal integration, which involves a detector and a spatial-temporal integrator unit that applies randomly generated measurement bases to detected luminance values over time, summing the results to obtain a set of compressive measurements, effectively capturing more video information with both spatial and temporal integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional compressive imaging performs only spatial projection/integration using snapshots, then the measurement process is simplified, but information is lost between snapshots or large amounts of data must be acquired to capture fast actions
Solution Approach 1:
The patent transitions from conventional spatial-only integration to spatio-temporal integration by adding the time dimension to the measurement process. The measurement matrix is extended to operate across spatial dimensions (MxN) and temporal dimension (L), creating a three-dimensional integration framework that captures information continuously across space and time rather than in discrete snapshots.
Solution Approach 2:
The patent implements continuous measurement acquisition across time by integrating signals over a temporal period rather than using discrete snapshots. The spatio-temporal measurement matrix applies continuous integration across the temporal dimension, ensuring no information is lost between measurement instances and maintaining continuous useful action throughout the measurement process.
2Reliability
If large amounts of raw image or video data are acquired to capture fast actions, then information integrity is maintained, but the acquisition cost and computational demand increase
Solution Approach 1:
The patent performs integration across the temporal dimension as a preliminary action before final measurement extraction. By pre-integrating signals over time and applying the spatio-temporal measurement matrix in advance, the system prepares compressed measurements that inherently preserve information integrity without requiring subsequent processing of large raw datasets.
Solution Approach 2:
The patent changes the fundamental parameters of the measurement process by extending the measurement matrix from spatial dimensions only to include temporal dimensions. This parameter change transforms the measurement approach from discrete spatial sampling to continuous spatio-temporal integration, achieving both information integrity and acquisition efficiency simultaneously.
3Measurement precision
If conventional methods acquire full N-sample signal and compute complete transform coefficients, then measurement accuracy is maximized, but the process becomes inefficient when N is large and K is small
Solution Approach 1:
The patent extracts only the essential information needed for accurate reconstruction by applying the spatio-temporal measurement matrix directly to the raw signal. Instead of computing all N transform coefficients and then selecting the K largest, the system extracts K compressive measurements directly through integrated spatio-temporal projection, removing unnecessary computational steps while preserving measurement precision.
Solution Approach 2:
The patent applies partial action by computing only the necessary K compressive measurements rather than the complete set of N transform coefficients. The spatio-temporal integration framework enables accurate reconstruction using fewer than N measurements, performing just enough computation to achieve the required measurement precision without excessive processing.
4Device complexity
If spatial-only integration is used in compressive imaging, then device complexity is reduced, but video data capture efficiency decreases
Solution Approach 1:
The patent adds the temporal dimension to the integration structure, transforming it from a two-dimensional spatial integration framework to a three-dimensional spatio-temporal framework. This dimensional extension enables the system to capture video data efficiently by utilizing time as an additional integration dimension without significantly increasing hardware complexity.
Solution Approach 2:
The spatio-temporal measurement matrix serves multiple functions simultaneously: it performs spatial integration across the image sensor array, temporal integration across the video sequence, and compressive measurement extraction. This multi-functional approach increases video capture efficiency while maintaining relatively simple device architecture through a unified measurement framework.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The present invention relates to an apparatus (100) and method for generating compressive measurements of video using spatial- temporal integration. The apparatus (100) includes a detector (110) configured to detect luminance values of a temporal video structure over a period of time based on optical data. The temporal video structure has pixels with a horizontal dimension and a vertical dimension with corresponding luminance values over the period of time. The apparatus (100) also includes a spatial-temporal integrator unit (110) configured to receive a plurality of measurement bases. Also, the spatial-temporal integrator unit is configured to apply each measurement basis to the temporal video structure and to sum resulting values for each measurement basis over the period of time to obtain a set of measurements. The summed values for each measurement basis is the set of measurements.