Search Results (5)

With the increasing frequency and severity of disasters and accidents, there is a growing need for efficient emergency alert systems. The ultra-high definition (UHD) broadcasting service based on Advanced Television Systems Committee (ATSC) 3.0, a leading terrestrial digital broadcasting system, offers such capabilities, including a wake-up function for minimizing damage through early alerts. In case of a disaster situation, the emergency alert wake-up signal is transmitted, allowing UHD TVs to be activated, enabling individuals to receive emergency alerts and access emergency broadcasting content. However, conventional methods for detecting the bootstrap signal, essential for this function, typically require an ATSC 3.0 demodulator. In this paper, we propose a novel deep learning-based method capable of detecting an emergency wake-up signal without the need for an ATSC 3.0. The proposed method leverages deep learning techniques, specifically a deep neural network (DNN) structure for bootstrap detection and a convolutional neural network (CNN) structure for wake-up signal demodulation and to detect the bootstrap and 2 bit emergency alert wake-up signal. Specifically, our method eliminates the need for Fast Fourier Transform (FFT), frequency synchronization, and interleaving processes typically required by a demodulator. By applying a deep learning in the time domain, we simplify the detection process, allowing for the detection of an emergency alert signal without the full suite of demodulator components required for ATSC 3.0. Furthermore, we have verified the performance of the deep learning-based method using ATSC 3.0-based RF signals and a commercial Software-Defined Radio (SDR) platform in a real environment. Full article

Terrestrial, satellite, and internet High Definition (HD) and Ultra High Definition (UHD) broadcasting have experienced notable advancements in recent years, yet the potential within the DTV Essential Hidden Area (DEHA) in each frame remains largely untapped. This study focuses on exploring and harnessing the capabilities of the DEHA via the introduction of a DEHA decoder. Through experimental analysis, the proposed decoder effectively reveals previously unnoticed DEHA in diverse HDTV broadcasting systems. By incorporating overlaid three-digit values, DEHA text, a 16 × 15 checked board pattern, and a QR code, the decoder enables the easy identification and extraction of the embedded DEHA service. Furthermore, the research investigates the potential of utilizing the embedded DEHA for transmitting program-related metadata, encompassing technical information, specific camera details, and post-production technologies. Leveraging a checked pattern block image, a seamless and efficient transfer mechanism within the embedded DEHA is established. The utilization of the embedded DEHA holds promising opportunities for enhancing DTV service, leveraging its inherent synchronization advantages within video content. Moreover, compliance with established broadcasting standards, such as ATSC, ISDB, and DVB, ensures compatibility and interoperability. This study emphasizes the significance of the DEHA in terrestrial, satellite, and internet broadcasting, unveiling new possibilities for innovation and improvement in the industry. Full article

Video compression technology for Ultra-High Definition (UHD) and 8K UHD video has been established and is being widely adopted by major broadcasting companies and video content providers, allowing them to produce high-quality videos that meet the demands of today’s consumers. However, high-resolution video content broadcasting is not an easy problem to be resolved in the near future due to limited resources in network bandwidth and data storage. An alternative solution to overcome the challenges of broadcasting high-resolution video content is to downsample UHD or 8K video at the transmission side using existing infrastructure, and then utilizing Video Super-Resolution (VSR) technology at the receiving end to recover the original quality of the video content. Current deep learning-based methods for Video Super-Resolution (VSR) fail to consider the fact that the delivered video to viewers goes through a compression and decompression process, which can introduce additional distortion and loss of information. Therefore, it is crucial to develop VSR methods that are specifically designed to work with the compression–decompression pipeline. In general, various information in the compressed video is not utilized enough to realize the VSR model. This research proposes a highly efficient VSR network making use of data from decompressed video such as frame type, Group of Pictures (GOP), macroblock type and motion vector. The proposed Convolutional Neural Network (CNN)-based lightweight VSR model is suitable for real-time video services. The performance of the model is extensively evaluated through a series of experiments, demonstrating its effectiveness and applicability in practical scenarios. Full article

Ongoing developments in video resolution either using consumer-grade or professional cameras has opened opportunities for different applications such as in sports events broadcasting and digital cinematography. In the field of geoinformation science and photogrammetry, image-based 3D city modeling is expected to benefit from this technology development. Highly detailed 3D point clouds with low noise are expected to be produced when using ultra high definition UHD videos (e.g., 4K, 8K). Furthermore, a greater benefit is expected when the UHD videos are captured from the air by consumer-grade or professional drones. To the best of our knowledge, no studies have been published to quantify the expected outputs when using UHD cameras in terms of 3D modeling and point cloud density. In this paper, a quantification is shown about the expected point clouds and orthophotos qualities when using UHD videos from consumer-grade drones and a review of which applications they can be applied in. The results show that an improvement in 3D models of ≅65% relative accuracy and ≅90% in point density can be attained when using 8K video frames compared with HD video frames which will open a wide range of applications and business cases in the near future. Full article

This paper proposes a live broadcast traffic model for an internet protocol (IP)-based terrestrial digital broadcasting system to transmit dynamic adaptive streaming over hypertext transfer protocol (DASH) media. The IP-based terrestrial digital broadcasting systems such as Advanced Television Systems Committee (ATSC) 3.0 transmit media content (e.g., full high definition and ultra-high definition) in units of DASH segment files. Although the DASH segment file has the same quality and playback time, the size of each DASH segment file can vary according to the media composition. The transmission resource of the terrestrial broadcasting system has increased the transmission capacity of broadcasting with new technologies. However, the transmission capacity is still limited and fixed compared to wired broadcasting networks. Therefore, a problem occurs with the efficiency of broadcasting resources and transmission delay when transmitting a variable segment file to a terrestrial digital broadcasting network. In this paper, the resource efficiency and transmission delay results that occur when transmitting the actual DASH segment file are simulated through the live broadcast traffic model, and the maximum delay time that a viewer accessing the terrestrial broadcast can experience is presented. Full article