Joint Data Hiding and Compression Scheme Based on Modified BTC and Image Inpainting

Liu, Xiao Long; Lin, Chia-Chen; Muhammad, Khan; Al‐Turjman, Fadi; Yuan, Shyan-Ming

doi:10.1109/access.2019.2935907

Cited by 13 publications

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main challenge is to decrease the consumption of energy during the working of the node which is that the actual data transmission will be doing. The parameters duty cycle energy consumption for baseline, offline and recovery algorithm is calculated with the optimization algorithm as a genetic algorithm [24][25][26][27].…”

Section: Related Workmentioning

confidence: 99%

A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications

et al. 2021

View full text Add to dashboard Cite

The internet of things (IoT) comprises various sensor nodes for monitoring physiological signals, for instance, electrocardiogram (ECG), electroencephalogram (EEG), blood pressure, and temperature, etc., with various emerging technologies such as Wi-Fi, Bluetooth and cellular networks. The IoT for medical healthcare applications forms the internet of medical things (IoMT), which comprises multiple resource-restricted wearable devices for health monitoring due to heterogeneous technological trends. The main challenge for IoMT is the energy drain and battery charge consumption in the tiny sensor devices. The non-linear behavior of the battery uses less charge; additionally, an idle time is introduced for optimizing the charge and battery lifetime, and hence the efficient recovery mechanism. The contribution of this paper is three-fold. First, a novel adaptive battery-aware algorithm (ABA) is proposed, which utilizes the charges up to its maximum limit and recovers those charges that remain unused. The proposed ABA adopts this recovery effect for enhancing energy efficiency, battery lifetime and throughput. Secondly, we propose a novel framework for IoMT based pervasive healthcare. Thirdly, we test and implement the proposed ABA and framework in a hardware platform for energy efficiency and longer battery lifetime in the IoMT. Furthermore, the transition of states is modeled by the deterministic mealy finite state machine. The Convex optimization tool in MATLAB is adopted and the proposed ABA is compared with other conventional methods such as battery recovery lifetime enhancement (BRLE). Finally, the proposed ABA enhances the energy efficiency, battery lifetime, and reliability for intelligent pervasive healthcare.

show abstract

Section: Related Workmentioning

confidence: 99%

A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The performance measures considered in this research is depicted in Eq. ( 1) to (6) [24][25][26][27][28][29][30]:…”

Section: Performance Evaluationmentioning

confidence: 99%

Automated Segmentation of COVID-19 Lesion from Lung CT Images Using U-Net Architecture

Kadry¹,

Al‐Turjman²,

Rajinikanth

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

Self Cite

View full text Add to dashboard Cite

Pneumonia caused by the novel Coronavirus Disease (COVID-19) is emerged as a global threat and considerably affected a large population globally irrespective of their age, race, and gender. Due to its rapidity and the infection rate, the World Health Organization (WHO) declared this disease as a pandemic. The proposed research work aims to develop an automated COVID-19 lesion segmentation system using the Convolutional Neural Network (CNN) architecture called the U-Net. The traditional U-Net scheme is employed to examine the COVID-19 infection present in the lung CT images. This scheme is implemented on the benchmark COVID-19 images existing in the literature (300 images) and the segmentation performance of the U-Net is confirmed by computing the essential performance measures using a relative assessment among the extracted lesion and the Ground-Truth (GT). The overall result attained with the proposed study confirms that, the U-Net scheme helps to get the better values for the performance values, such as Jaccard (>86%), Dice (>92%) and segmentation accuracy (>95%).

show abstract

“…Block truncation coding (BTC) and its variants have been playing an important role on image processing and computer vision applications, such as image/video compression [1][2][3], image watermarking [4,5], data hiding [3,6], image retrieval and classification [7][8][9][10], image restoration, [11][12][13] etc. Many efforts have been focused on further improving the performance of BTC and its variants, including the computational complexity reduction, decoded image quality improvement, and its applications, as reported in [1, 2, 7-9, 12, 13].…”

Section: Introductionmentioning

confidence: 99%

Halftoning-based BTC image reconstruction using patch processing with border constraint

2020

View full text Add to dashboard Cite

This paper presents a new halftoning-based block truncation coding (HBTC) image reconstruction using sparse representation framework. The HBTC is a simple yet powerful image compression technique, which can effectively remove the typical blocking effect and false contour. Two types of HBTC methods are discussed in this paper, i.e., ordered dither block truncation coding (ODBTC) and error diffusion block truncation coding (EDBTC). The proposed sparsity-based method suppresses the impulsive noise on ODBTC and EDBTC decoded image with a coupled dictionary containing the HBTC image component and the clean image component dictionaries. Herein, a sparse coefficient is estimated from the HBTC decoded image by means of the HBTC image dictionary. The reconstructed image is subsequently built and aligned from the clean, i.e. non-compressed image dictionary and predicted sparse coefficient. To further reduce the blocking effect, the image patch is firstly identified as "border" and "non-border" type before applying the sparse representation framework. Adding the Laplacian prior knowledge on HBTC decoded image, it yields better reconstructed image quality. The experimental results demonstrate the effectiveness of the proposed HBTC image reconstruction. The proposed method also outperforms the former schemes in terms of reconstructed image quality.

show abstract

Joint Data Hiding and Compression Scheme Based on Modified BTC and Image Inpainting

Cited by 13 publications

References 32 publications

A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications

A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications

Automated Segmentation of COVID-19 Lesion from Lung CT Images Using U-Net Architecture

Halftoning-based BTC image reconstruction using patch processing with border constraint

Contact Info

Product

Resources

About