Compressive Sensing Video Packets for Low-Power Loss-Tolerant Transmission
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Solution Overview
Problem
Video surveillance networks face challenges in transmitting compressed video data due to complexity and power consumption requirements, especially in low-power transmission and varying channel quality, which affects the reliability and payload data rate.
Innovation Solution
Implementing compressive sensing (CS) with a simple, low-power protocol for video data transmission, using a transmitting device to prepare packets with packet offset values and block identifiers, and a receiving device to process and reconstruct video data blocks even with lost packets, reducing the need for error correction and retransmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional video coding (H.264/H.265) is used with reliable communication techniques, then video compression efficiency is improved, but device complexity and power consumption increase significantly
Solution Approach 1:
The patent segments video data into blocks and applies compressive sensing to each block, transmitting only essential features rather than full compressed video streams. This segmentation approach reduces the complexity of communication protocols while maintaining acceptable video quality, as the receiver can reconstruct video from these segmented features without requiring complex decompression algorithms.
Solution Approach 2:
The patent replaces the mechanical system of conventional video coding (complex encoding/decoding algorithms) with a different approach based on compressive sensing theory. Instead of using traditional compression standards like H.264/H.265 that require complex processors, the system uses random projection matrices and simpler reconstruction algorithms, substituting the entire compression mechanism to reduce device complexity.
2Reliability
If forward error correction and retransmission techniques are applied, then transmission reliability is improved, but available payload data rate decreases significantly
Solution Approach 1:
The patent applies preliminary action by embedding redundancy and error correction capabilities directly into the compressive sensing measurement process itself, rather than adding separate error correction layers afterward. The random projection matrix is designed to inherently provide robustness against packet loss, so error correction is built into the compression framework from the beginning, maintaining high payload rates while ensuring reliability.
Solution Approach 2:
The patent changes the fundamental parameters of how video data is represented and transmitted. By transforming video into the compressive sensing domain with appropriate measurement matrices and parameters, the system achieves inherent robustness to channel errors. The reconstruction process can tolerate missing packets by adjusting reconstruction parameters, maintaining both reliability and high data rates without traditional error correction overhead.
3Productivity
If compression techniques are applied to reduce data rate, then bandwidth utilization is improved, but sensitivity to channel errors increases
Solution Approach 1:
The patent applies beforehand cushioning by designing the compressive sensing measurement process to anticipate and compensate for channel errors before transmission occurs. The random projection matrix and measurement process are configured to create redundant information that cushions against potential packet loss during transmission, allowing the system to maintain high compression ratios while being robust to channel errors through pre-built resilience.
4Use of energy by moving object
If low power transmission is used to reduce energy consumption, then power usage is improved, but signal-to-noise ratio in received signal decreases
Solution Approach 1:
The patent changes transmission parameters by sending compressive sensing measurements rather than full video data, which requires lower transmission power. The measurement process is configured with parameters that optimize the signal-to-noise ratio at the lower power level, using appropriate measurement matrix designs that maintain reconstruction quality even with reduced transmission power and lower SNR in the received signal.
Data Source
AI summary
In one example embodiment, a transmitting device includes a memory configured to store computer-readable instructions and a processor configured to execute the computer-readable instructions. The processor is configured to prepare at least one packet, the at least one packet including a portion of a compressed representation of at least one data block, a packet offset value and a block identifier, the block identifier identifying the at least one data block, the packet offset value identifying the portion of the compressed representation of the at least one data block within the compressed representation of the at least one data block and broadcast the at least one packet.