Low-complexity Three-dimensional Discrete Hartley Transform Approximations for Medical Image Compression

TL;DR

This paper introduces multiplierless 3D DHT approximations for medical image compression, significantly reducing computational complexity and execution time, suitable for resource-constrained biomedical devices.

Contribution

It proposes novel fixed-point, multiplierless 3D DHT approximations derived via tensor formalism, enabling efficient implementation in low-power medical imaging devices.

Findings

Achieves over 98% SSIM in lossy compression

Reduces multiplicative complexity by 100%

Decreases execution time by up to 90% on ARM Cortex-M0+

Abstract

The discrete Hartley transform (DHT) is a useful tool for medical image coding. The three-dimensional DHT (3D DHT) can be employed to compress medical image data, such as magnetic resonance and X-ray angiography. However, the computation of the 3D DHT involves several multiplications by irrational quantities, which require floating-point arithmetic and inherent truncation errors. In recent years, a significant progress in wireless and implantable biomedical devices has been achieved. Such devices present critical power and hardware limitations. The multiplication operation demands higher hardware, power, and time consumption than other arithmetic operations, such as addition and bit-shifts. In this work, we present a set of multiplierless DHT approximations, which can be implemented with fixed-point arithmetic. We derive 3D DHT approximations by employing tensor formalism. Such proposed methods present prominent computational savings compared to the usual 3D DHT approach, being appropriate for devices with limited resources. The proposed transforms are applied in a lossy 3D DHT-based medical image compression algorithm, presenting practically the same level of visual quality ($>98\%$ in terms of SSIM) at a considerable reduction in computational effort ($100 \%$ multiplicative complexity reduction). Furthermore, we implemented the proposed 3D transforms in an ARM Cortex-M0+ processor employing the low-cost Raspberry Pi Pico board. The execution time was reduced by $\sim$70% compared to the usual 3D DHT and $\sim$90% compared to 3D DCT.