Towards a Low Complexity Scheme for Medical Images in Scalable Video Coding

Abstract

Medical imaging has become of vital importance for diagnosing diseases and conducting noninvasive procedures. Advances in eHealth applications are challenged by the fact that Digital Imaging and Communications in Medicine (DICOM) requires high-resolution images, thereby increasing their size and the associated computational complexity, particularly when these images are communicated over IP and wireless networks. Therefore, medical research requires an efficient coding technique to achieve high-quality and low-complexity images with error-resilient features. In this study, we propose an improved coding scheme that exploits the content features of encoded videos with low complexity combined with flexible macroblock ordering for error resilience. We identify the homogeneous region in which the search for optimal macroblock modes is early terminated. For non-homogeneous regions, the integration of smaller blocks is employed only if the vector difference is less than the threshold. Results confirm that the proposed technique achieves a considerable performance improvement compared with existing schemes in terms of reducing the computational complexity without compromising the bit-rate and peak signal-to-noise ratio.INDEX TERMS eHealth, medical imaging, scalable video coding, flexible macroblock ordering, slice coding, low complexity.

Simulation of Video Image Fault Tolerant Coding Transmission in Digital Multimedia

“…e three parameters of spatial contrast sensitivity function, brightness adaptive factor, and contrast masking factor of the digital multimedia video image are calculated, respectively. According to the product of the three parameters, the transform domain JND model of the digital multimedia video image is established, and the model is used for the quantization and coding of multimedia video multiresolution image [10,11].…”

An empirical investigation into the altering health perspectives in the internet of health things

“… Analysis the daily activities [ 74 ] Detect the pests and diseases of the skin by image processing [ 75 , 76 ] Studying and understanding the chronic diseases [ 77 ] Predict about infected cased of new virus like covid-19 [ 78 ] Voice Recognition to provide voice command and help disabled users [ 79 ]. Data analysis by machine learning (ML), statistics, reports and visualization, medical image processing, early detection [ 80 ]–[ 82 ]. Ambient Assisted Living: This domain concerns disabled users, the elderly, and people with chronic diseases.…”

“…Data analysis by machine learning (ML), statistics, reports and visualization, medical image processing, early detection [ 80 ]–[ 82 ].…”

On Analyzing Video Transmission Over Wireless WiFi and 5G C-Band in Harsh IIoT Environments

“…For given network QoS parameters, many approaches have been adopted to improve the quality of the video transmission at different layers: coding [2], [3], [4], streaming layer [5], [6] and network [7], [8].…”

“…In order to understand the impact of industrial environment into the quality of the VR/AR video perceived by the employees (of the factory), we perform a series of measurements of network parameters (rate, packet loss ratio and latency) under three conditions: (1) when all the industrial machines are off, (2) in the moment when the machines are being switched on, and (3) when all the industrial machines are working in a normal functioning way.…”