Hybrid Spectral Image Compression via Fourier-Wavelet Filtering
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Solution Overview
Problem
Existing spectral image compression methods introduce artifacts and degrade image quality, failing to provide sufficient compression for large spectral images transmitted from spacecraft, which exceeds bandwidth capacity.
Innovation Solution
A hybrid compression technique combining Fourier decomposition in the spectral dimension and wavelet decomposition in spatial dimensions, filtering out higher frequency coefficients and bit quantizing lower frequency coefficients, with varying compression parameters based on information content across planes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing compression schemes are applied to spectral image data, then the data size is reduced and bandwidth requirements are met, but image quality is degraded and artifacts are introduced
Solution Approach 1:
The spectral image data is segmented into multiple wavelength planes, which are then processed independently through the compression algorithm. This segmentation allows selective retention of important spectral information while compressing less critical data, thereby maintaining image quality while reducing data size.
Solution Approach 2:
The patent applies parameter changes by using different compression techniques and thresholds for different wavelength planes. By dynamically adjusting compression parameters based on the information content of each plane, the system achieves optimal balance between data reduction and quality preservation.
2Productivity
If higher compression ratios are applied to meet bandwidth capacity, then transmission feasibility is achieved, but image fidelity is lost and analysis capability is adversely affected
Solution Approach 1:
Different regions of the spectral data are treated with different compression aggressiveness based on their information content. Critical spectral regions retain higher fidelity while less critical regions undergo more aggressive compression, achieving local optimization of the quality-compression tradeoff.
Solution Approach 2:
The compression algorithm dynamically adjusts its parameters based on the characteristics of each wavelength plane. By adapting the compression strength to the local information content, the system maintains fidelity where needed while achieving high compression where possible.
Data Source
AI summary
A method and system for generating a compressed spectral image is provided. Spectral image data including a plurality of spectral intensity values is received. The spectral intensity values are associated with a first spatial dimension (x-dimension), a second spatial dimension (y-dimension) and a wavelength dimension (λ-dimension). A window is applied to the spectral image data along the λ-dimension, to select a subset of the spectral image data corresponding to a range of wavelengths. A Fourier transform is performed on the windowed spectral image data along the λ-dimension, at locations along the x-dimension and y-dimension, to generate Fourier coefficients associated with each of the locations. The Fourier transformed data is filtered by retaining a subset of the Fourier coefficients at each of the locations. Wavelet compression is performed on the filtered data along the x-dimension and the y-dimension to generate the compressed spectral image.