SAR Image Compression With DCT Subbands for Bandwidth-Limited Transmission
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Solution Overview
Problem
The challenge of efficiently compressing and transmitting large synthetic aperture radar (SAR) image data is exacerbated by limited bandwidth, high latency, and costly data transmission, particularly in satellite communication, which affects real-time imaging applications and storage constraints in remote environments.
Innovation Solution
A novel compression technique for SAR images using block-wise discrete cosine transform (DCT) decomposition, followed by subband grouping and latent feature learning, with arithmetic coding, optimized for hardware acceleration on multi-core systems and FPGA-based systems, to create a compressed bitstream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SAR image data is transmitted in high resolution without compression, then image quality is maintained, but transmission bandwidth requirements increase and transmission costs increase
Solution Approach 1:
The SAR image is divided into multiple subbands using discrete cosine transform (DCT) decomposition. This segmentation allows different parts of the image to be processed and compressed at different levels, enabling efficient bandwidth utilization while preserving essential image quality characteristics.
Solution Approach 2:
The patent transforms the image from spatial domain to frequency domain using DCT, changing the representation parameters. This transformation enables more efficient compression by allowing selective retention of important frequency components while discarding less significant ones, thus reducing transmission volume while maintaining perceived image quality.
2Quantity of substance
If SAR image data is compressed using traditional compression methods, then data transmission volume is reduced, but compression efficiency is insufficient for SAR image properties
Solution Approach 1:
The patent applies different processing strategies to different subbands based on their local characteristics. Low-frequency subbands that contain important image information are preserved with higher fidelity, while high-frequency subbands are compressed more aggressively. This local quality approach optimizes compression efficiency while maintaining essential image properties.
Solution Approach 2:
The patent creates a transformed representation (DCT coefficients) that copies the essential information from the original SAR image in a more compressible format. This copied representation in the frequency domain allows for more efficient compression while enabling reconstruction of the original image quality.
3Loss of time
If SAR images are processed and transmitted in real-time, then response time is improved, but processing power requirements and energy consumption increase
Solution Approach 1:
By segmenting the SAR image into subbands, the processing task is divided into smaller, more manageable units. This segmentation enables parallel processing of different subbands, reducing overall processing time and enabling real-time operation while distributing energy consumption across multiple processing units.
Solution Approach 2:
The patent extracts and processes only the essential frequency components needed for maintaining image quality, rather than processing the entire high-resolution image data. This extraction approach reduces the computational burden and energy consumption while still achieving real-time processing capabilities.
Data Source
AI summary
For compressing synthetic aperture radar (SAR) images, preprocessing operations are performed on an input SAR image. A discrete cosine transform is performed on the image, and multiple subbands are created, where each subband represents a particular range of frequencies. The subbands are organized into multiple groups, where the multiple groups comprise a first low frequency group, a second low frequency group, and a high frequency group. A latent space representation is generated corresponding to each of the multiple groups of subbands. Compression is performed on one or more of the subbands, thereby creating a compressed bitstream, wherein the compressed bitstream is a compressed version of the input SAR image.