Open Access

ARTICLE

Medical Image Compression Method Using Lightweight Multi-Layer Perceptron for Mobile Healthcare Applications

by Taesik Lee¹, Dongsan Jun^1,*, Sang-hyo Park², Byung-Gyu Kim³, Jungil Yun⁴, Kugjin Yun⁴, Won-Sik Cheong⁴

1 Kyungnam University, Changwon, 51767, Korea
2 Kyungpook National University, Daegu, 41566, Korea
3 Sookmyung Women’s University, Seoul, 04310, Korea
4 Electronics and Telecommunications Research Institute, Daejeon, 34129, Korea

* Corresponding Author: Dongsan Jun. Email:

(This article belongs to the Special Issue: Integrity and Multimedia Data Management in Healthcare Applications using IoT)

Computers, Materials & Continua 2022, 70(1), 2013-2029. https://doi.org/10.32604/cmc.2022.019604

Received 19 April 2021; Accepted 14 June 2021; Issue published 07 September 2021

Abstract

As video compression is one of the core technologies required to enable seamless medical data streaming in mobile healthcare applications, there is a need to develop powerful media codecs that can achieve minimum bitrates while maintaining high perceptual quality. Versatile Video Coding (VVC) is the latest video coding standard that can provide powerful coding performance with a similar visual quality compared to the previously developed method that is High Efficiency Video Coding (HEVC). In order to achieve this improved coding performance, VVC adopted various advanced coding tools, such as flexible Multi-type Tree (MTT) block structure which uses Binary Tree (BT) split and Ternary Tree (TT) split. However, VVC encoder requires heavy computational complexity due to the excessive Rate-distortion Optimization (RDO) processes used to determine the optimal MTT block mode. In this paper, we propose a fast MTT decision method with two Lightweight Neural Networks (LNNs) using Multi-layer Perceptron (MLP), which are applied to determine the early termination of the TT split within the encoding process. Experimental results show that the proposed method significantly reduced the encoding complexity up to 26% with unnoticeable coding loss compared to the VVC Test Model (VTM).

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Keywords

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