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Lensless Fluorescent Microscopy on a Chip

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Edge-Aware Extended Star-Tetrix Transforms for CFA-Sampled Raw Camera Image Compression.

Taizo Suzuki, Liping Huang

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|September 15, 2022

Summary

New spectral-spatial transforms (XSTTs) and edge-aware versions (EXSTTs) efficiently compress raw camera images. These transforms decorrelate color spaces, improving compression performance over conventional methods, especially for images with many edges.

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Area of Science:

Image processing
Digital signal processing
Data compression

Background:

Raw camera images captured with a color filter array (CFA-sampled raw images) require efficient compression.
Existing spectral-spatial transforms offer compression but can be improved, especially for edge-rich images.

Purpose of the Study:

To introduce novel spectral-spatial transforms (XSTTs) and their edge-aware variants (EXSTTs) for efficient CFA-sampled raw image compression.
To evaluate the performance of XSTTs and EXSTTs in decorrelating color spaces and improving compression efficiency.

Main Methods:

Developed two types of extended Star-Tetrix transforms (XSTTs) and their edge-aware versions (EXSTTs).
XSTTs are based on extending the Star-Tetrix transform (STT) and wavelet-based spectral-spatial transforms.
EXSTTs incorporate weighting along image edge directions to enhance decorrelation.

Main Results:

EXSTTs significantly reduce energy differences between green components in CFA-sampled raw images (3.38-30.08% for high-quality, 8.97-14.47% for mobile images).
XSTTs/EXSTTs demonstrate competitive or superior performance compared to conventional methods in JPEG 2000 lossless and lossy compression.
Type-I EXSTT and XSTTs show notable bitrate and peak signal-to-noise ratio improvements for high-quality images, particularly those with many edges.

Conclusions:

XSTTs and EXSTTs offer an efficient method for compressing CFA-sampled raw images with minimal complexity and no extra bits.
The proposed transforms provide significant improvements in decorrelation and compression performance, especially for edge-heavy image content.
While previous methods excel for mobile images, XSTTs/EXSTTs show promising results and similar trends, indicating their potential for broad application.