Showing papers in "International Journal of Digital Multimedia Broadcasting in 2021"
TL;DR: The study concluded that the proposed secured medical information system was evidenced to be proficient in secreting medical information and creating undetectable stego images with slight entrenching falsifications when likened to other prevailing approaches.
Abstract: Safe conveyance of medical data across unsecured networks nowadays is an essential issue in telemedicine With the exponential growth of multimedia technologies and connected networks, modern healthcare is a huge step ahead Authentication of a diagnostic image obtained from a specialist at a remote location which is from the sender is one of the most challenging tasks in an automated healthcare setup Intruders were found to be able to efficiently exploit securely transmitted messages from previous literature since the algorithms were not efficient enough leading to distortion of information Therefore, this study proposed a modified least significant bit (LSB) technique capable of protecting and hiding medical data to solve the crucial authentication issue The application was executed and established by utilizing MATLAB 2018a, and it used a logical bit shift operation for execution The investigational outcomes established that the proposed technique can entrench medical information without leaving a perceptible falsification in the stego image The result of this implementation shows that the modified LSB image steganography outperformed the standard LSB technique with a higher PSNR value and lower MSE value when compared with previous research works The number of shifts was added as a new performance metric for the proposed system The study concluded that the proposed secured medical information system was evidenced to be proficient in secreting medical information and creating undetectable stego images with slight entrenching falsifications when likened to other prevailing approaches
9 citations
TL;DR: In this article, the authors used adaptive channel switching with a personalized electronic program guide to resolve the zapping delay in IPTV systems, which saves the subscribers the time of channel navigation by eliminating the need to search for channels they want to view.
Abstract: The pervasive acceptability of a revolution from monodirectional push-based media broadcasting to a bidirectional interactive pull-based internet protocol television (IPTV) has spotted significant development in recent years. The pervasive acceptability is because of the mammoth number of exhilarating television (TV) channels that IPTV offers. However, the channel switching feature of a TV system requires additional development despite the increased implementation of IPTV systems worldwide. Subscribers of IPTV services must be able to swiftly explore live TV stations and video contents of interest seamlessly, but zapping delay is a deterrent that occurs during a channel change that causes a significant glitch in IPTV systems. Many of the literature approaches such as channel prediction based on behavior analysis have shown flaws in resolving zapping delay. The approach of this study uses adaptive channel switching with a personalized electronic program guide to resolving zapping delay. The resolution saves the subscribers the time of channel navigation by eliminating the need to search for channels they want to view.
2 citations
TL;DR: A two-stage generative adversarial network model is proposed to reconstruct realistic superresolution images from four times downsampled infrared images to solve the problems of low resolution and unclear edges of infrared images.
Abstract: The convolutional neural network has achieved good results in the superresolution reconstruction of single-frame images. However, due to the shortcomings of infrared images such as lack of details, poor contrast, and blurred edges, superresolution reconstruction of infrared images that preserves the edge structure and better visual quality is still challenging. Aiming at the problems of low resolution and unclear edges of infrared images, this work proposes a two-stage generative adversarial network model to reconstruct realistic superresolution images from four times downsampled infrared images. In the first stage of the generative adversarial network, it focuses on recovering the overall contour information of the image to obtain clear image edges; the second stage of the generative adversarial network focuses on recovering the detailed feature information of the image and has a stronger ability to express details. The infrared image superresolution reconstruction method proposed in this work has highly realistic visual effects and good objective quality evaluation results.
2 citations
TL;DR: A fast FVC algorithm to achieve better performance and to reduce encoding complexity is provided and the experimental results prove that the time saving reaches 13% with a decrease in the Bitrate and in the PSNR.
Abstract: Future Video Coding (FVC) is a modern standard in the field of video coding that offers much higher compression efficiency than the HEVC standard. FVC was developed by the Joint Video Exploration Team (JVET), formed through collaboration between the ISO/IEC MPEG and ITU-T VCEG. New tools emerging with the FVC bring in super resolution implementation schemes that are being recommended for Ultra-High-Definition (UHD) video coding in both SDR and HDR images. However, a new flexible block structure is adopted in the FVC standard, which is named quadtree plus binary tree (QTBT) in order to enhance compression efficiency. In this paper, we provide a fast FVC algorithm to achieve better performance and to reduce encoding complexity. First, we evaluate the FVC profiles under All Intra, Low-Delay P, and Random Access to determine which coding components consume the most time. Second, a fast FVC mode decision is proposed to reduce encoding computational complexity. Then, a comparison between three configurations, namely, Random Access, Low-Delay B, and Low-Delay P, is proposed, in terms of Bitrate, PSNR, and encoding time. Compared to previous works, the experimental results prove that the time saving reaches 13% with a decrease in the Bitrate of about 0.6% and in the PSNR of 0.01 to 0.2 dB.
1 citations
TL;DR: This paper proposes an algorithm that deploys the hierarchical clustering to discover the inner connection of data and represents the entire sequence using some of trajectories (in this paper, these trajectories are referred to as the set of anchor trajectories) to improve the affinity coefficient and strong connection between trajectories in the same subspace.
Abstract: Sparse subspace clustering (SSC) is one of the latest methods of dividing data points into subspace joints, which has a strong theoretical guarantee. However, affine matrix learning is not very effective for segmenting multibody nonrigid structure from motion. To improve the segmentation performance and efficiency of the SSC algorithm in segmenting multiple nonrigid motions, we propose an algorithm that deploys the hierarchical clustering to discover the inner connection of data and represents the entire sequence using some of trajectories (in this paper, we refer to these trajectories as the set of anchor trajectories). Only the corresponding positions of the anchor trajectories have nonzero weights. Furthermore, in order to improve the affinity coefficient and strong connection between trajectories in the same subspace, we optimise the weight matrix by integrating the multilayer graphs and good neighbors. The experiments prove that our methods are effective.
1 citations
Abstract: Vehicle reidentification refers to the mission of matching vehicles across nonoverlapping cameras, which is one of the critical problems of the intelligent transportation system. Due to the resemblance of the appearance of the vehicles on road, traditional methods could not perform well on vehicles with high similarity. In this paper, we utilize hypergraph representation to integrate image features and tackle the issue of vehicles re-ID via hypergraph learning algorithms. A feature descriptor can only extract features from a single aspect. To merge multiple feature descriptors, an efficient and appropriate representation is particularly necessary, and a hypergraph is naturally suitable for modeling high-order relationships. In addition, the spatiotemporal correlation of traffic status between cameras is the constraint beyond the image, which can greatly improve the re-ID accuracy of different vehicles with similar appearances. The method proposed in this paper uses hypergraph optimization to learn about the similarity between the query image and images in the library. By using the pair and higher-order relationship between query objects and image library, the similarity measurement method is improved compared to direct matching. The experiments conducted on the image library constructed in this paper demonstrates the effectiveness of using multifeature hypergraph fusion and the spatiotemporal correlation model to address issues in vehicle reidentification.
TL;DR: In this article, the authors proposed a multi-session multicast (MSM) system to optimally allocate the wireless resources to tiles with different qualities to maximize the expected user experience, and the experimental results show that the proposed MSM system provides higher quality videos to viewers using limited wireless resources.
Abstract: 360-degree video content provides a rich and immersive multimedia experience to viewers by allowing viewers to the video from any angle. However, 360-degree videos require much higher bandwidth to be delivered over mobile networks compared to conventional videos. Multicasting of the videos is one of the solutions to efficiently utilize the limited bandwidth since many viewers share the wireless spectrum resource for popular videos, such as sports events or musical concerts. LTE eMBMS assigns the videos to the video sessions, and multiple viewers can subscribe to the same video allocated to the video sessions. Moreover, the tiling of the 360-degree video makes it possible to control the regional quality of the video. The tiles that are likely to be seen by many viewers should have higher quality than other tiles to satisfy more viewers. In this paper, we proposed the Multi-Session Multicast (MSM) system to optimally allocate the wireless resources to tiles with different qualities to maximize the expected user experience. The experimental results show that the proposed MSM system provides higher quality videos to viewers using limited wireless resources.