Streaming Signal Reconstruction With Sliding-Window Sparse Sampling
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Solution Overview
Problem
Conventional methods for reconstructing sparse streaming signals face challenges in real-time processing, introducing artifacts and uncertainty in processing delay, especially for audio and video data, due to their reliance on discrete block processing and finite-length signal assumptions, which are not suitable for streaming signals.
Innovation Solution
A real-time method using a sliding window approach for continuous measurement and reconstruction of sparse streaming signals, ensuring computational efficiency and guaranteed performance by employing a causal measurement system with a finite response length, and satisfying the restricted isometry property within sliding windows, allowing for reconstruction in the time or frequency domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional CS methods process signals as discrete blocks, then reconstruction can be performed using finite dimensional methods, but blocking artifacts are introduced at boundaries between blocks
Solution Approach 1:
The patent divides the streaming signal into overlapping blocks with a sliding window approach, where each block is processed independently but overlaps with adjacent blocks. This segmentation allows finite dimensional reconstruction methods to be applied while the overlap region ensures continuity and reduces blocking artifacts at boundaries.
Solution Approach 2:
The patent employs a dynamic sliding window that continuously moves through the streaming signal, adjusting the processing block position in real-time. This dynamic approach allows the system to adapt to the streaming nature of the signal while maintaining the benefits of block-based processing and reducing boundary artifacts through continuous overlap.
2Productivity
If conventional CS methods use finite dimensional reconstruction, then processing can be completed for each block, but processing delay cannot be guaranteed for real-time systems
Solution Approach 1:
The patent performs preliminary compressive sampling at a reduced rate while maintaining the ability to reconstruct the full signal. By pre-processing the signal with randomized measurements and maintaining a dictionary of basis functions, the system prepares reconstruction data in advance, enabling guaranteed delay bounds for real-time processing.
Solution Approach 2:
The patent changes the sampling rate parameter from the traditional Nyquist rate to a lower rate compatible with the signal's sparsity characteristics. This parameter change, combined with adaptive dictionary selection, enables faster processing with guaranteed delay bounds by reducing the amount of data that needs to be processed while maintaining reconstruction quality.
3Measurement precision
If sampling is performed at Nyquist rate, then the signal can be represented without error, but resources are wasted for sparse signals that can be compressed
Solution Approach 1:
The patent exploits the inherent sparsity structure of the signal to enable efficient compression. By identifying and utilizing the sparse representation in a suitable basis, the system allows the signal itself to 'serve' the compression function, eliminating the need for oversampling while maintaining accurate reconstruction through adaptive dictionary selection and sparse coding.
Data Source
Figure 1A~1B
Figure 2A
Figure 2B
AI summary
A method reconstructs a streaming signal xn from streaming measurements by maintaining a working set of measurements, a working snapshot of a measurement system, an internal working signal estimate, and an external working signal estimate. Using a current working set of measurements, the internal working signal estimates, the working snapshot of the measurement system, and a model of a signal sparsity are refined. The external working signal estimate is refreshed. A subset of coefficients of the external working signal estimate is committed to an output. A next streaming measurement and a corresponding next measurement vector are received. The working set of measurements, the working snapshot of the measurement system, and the internal working signal estimate are updated to incorporate the next measurement and the corresponding measurement vector. Then, an oldest measurement and a corresponding oldest measurement vector, and an effect of the committed subset of coefficients are removed.