SAR Image Compression Using DCT Subbands and Latent Encoding
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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 network bandwidth, high latency, and costly data transmission, particularly in satellite communication, which affects real-time imaging applications and storage in space-constrained environments.
Innovation Solution
A system and method utilizing block-wise discrete cosine transform (DCT) decomposition, followed by a latent feature encoding network and arithmetic coding, to compress SAR images, suitable for parallelization on multi-core systems and FPGA/ASIC, enabling efficient compression and decompression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If SAR image data is transmitted without compression, then image quality is preserved, but network bandwidth consumption increases and transmission time increases
Solution Approach 1:
The patent transforms SAR image data from spatial domain to frequency domain using DCT, changing the representation parameters to enable efficient compression while preserving essential image information. This parameter transformation allows selective retention of important frequency components
Solution Approach 2:
The patent extracts and retains only the most important frequency subbands (low-frequency components) while discarding less important high-frequency components. This extraction approach maintains image quality for critical features while reducing data volume for transmission
2Quantity of substance
If SAR image data is compressed using traditional methods, then data volume is reduced, but compression efficiency is insufficient for SAR image characteristics
Solution Approach 1:
The patent applies different processing strategies to different frequency subbands of the SAR image. Low-frequency subbands are preserved with higher fidelity while high-frequency subbands are compressed more aggressively, matching the local importance of different image regions
Solution Approach 2:
The patent divides the SAR image into multiple frequency subbands using DCT decomposition. This segmentation allows independent processing of each subband, enabling optimized compression for each frequency range based on its contribution to image quality
3Adaptability or versatility
If SAR images are transmitted from satellites to ground stations, then remote sensing capabilities are utilized, but network bandwidth is limited and transmission costs increase
Solution Approach 1:
The patent transmits only the essential low-frequency components of SAR images at high fidelity, while high-frequency components are either compressed more aggressively or reconstructed at the ground station. This partial transmission approach reduces bandwidth consumption and transmission costs while maintaining adequate image quality for remote sensing applications
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. A first bitstream is created based on the latent space representation, and an alternate representation of the latent space is used for creating a second bitstream, enabling multiple-pass techniques for SAR image data compression, including phase unwrapping for supporting interferometric SAR (InSAR) applications.