Efficient HEVC-based video adaptation using transcoding

In a video transmission system, it is important to take into account the great diversity of the network/end-user constraints. On the one hand, video content is typically streamed over a network that is characterized by different bandwidth capacities. In many cases, the bandwidth is insufficient to transfer the video at its original quality. On the other hand, a single video is often played by multiple devices like PCs, laptops, and cell phones. Obviously, a single video would not satisfy their different constraints. These diversities of the network and devices capacity lead to the need for video adaptation techniques, e.g., a reduction of the bit rate or spatial resolution. Video transcoding, which modifies a property of the video without the change of the coding format, has been well-known as an efficient adaptation solution. However, this approach comes along with a high computational complexity, resulting in huge energy consumption in the network and possibly network latency. This presentation provides several optimization strategies for the transcoding process of HEVC (the latest High Efficiency Video Coding standard) video streams. First, the computational complexity of a bit rate transcoder (transrater) is reduced. We proposed several techniques to speed-up the encoder of a transrater, notably a machine-learning-based approach and a novel coding-mode evaluation strategy have been proposed. Moreover, the motion estimation process of the encoder has been optimized with the use of decision theory and the proposed fast search patterns. Second, the issues and challenges of a spatial transcoder have been solved by using machine-learning algorithms. Thanks to their great performance, the proposed techniques are expected to significantly help HEVC gain popularity in a wide range of modern multimedia applications

Publications on COVID-19 from Vietnam during 2020 and 2021: A bibliometric analysis

Background: Following the outbreak of the COVID-19 pandemic, published research from Vietnam related to the pandemic was analysed using bibliometrics. Objectives: To examine the status of research on COVID-19 by authors from Vietnam. Methods: The following bibliometric aspects were considered in the analysis: international collaboration, institutions from Vietnam and their partner institutions worldwide, subjects and topics, types of documents, and individual authors. The basis of the study was data obtained from the Scopus database between 2020 and 2021. The data were analysed using Microsoft Excel, R, and VOSviewer, and the emerging trends illustrated through descriptive analysis and science mapping.  Results: Between 2020 and 2021, researchers from Vietnam co-authored 1034 documents related to COVID-19, amounting to 0.35% of the total of 296,148 such documents published worldwide as ascertained from the Scopus database. Vietnam’s top country collaborators in that research were USA, Australia, the United Kingdom, India, and Taiwan ROC. The top Vietnam institutions were Duy Tan University, Ton Duc Thang University, and the University of Economics Ho Chi Minh City. The research from Vietnam covered many subjects, from medicine and natural sciences to social sciences and economics. Eight clusters of topics related to COVID-19 were identified. In terms of citations, the most highly cited documents were the outcome of collaboration with international authors. Lastly, the study ranked top authors based on either the number of publications or the number of citations.  Conclusion: This study provides a preliminary picture of studies related to COVID-19 co-authored by researchers in Vietnam. The picture may help the Vietnam government in devising appropriate strategies for post-COVID-19 restoration of the country’s socio-economic status

Induced systemic resistance against rice grassy stunt virus – a promising field for ecological rice production: Research article

Most rice protection methods have currently used toxic chemicals to control pathogens and pests, which leads to environmental pollution. Systemic acquired resistance (SAR) taking advantage of natural defence reaction of plants could be proposed as an alternative, ecologically friendly approach for plant protection. Its application into rice production could minimize the chemicals quantity used and could contribute to the decrease of environmental pollution and the development of sustainable agriculture. The research was conducted to select the most effective chemical and suitable method to improve the health of rice plants infected by grassy stunt disease in net-house of Can Tho University. SAR chemicals were used at very low concentrations (in mM). Results showed that the height of rice plants treated with SAR chemicals was higher than that of plants untreated. Besides, the number of diseased plants was reduced and the ratio of firm grain and yield increased when plants were applied by SAR. Among the used substances, oxalic acid provided the best systemic acquired resistance. With oxalic acid, seed soaking was better than seed coating in systemic acquired resistance against rice grassy stunt disease.Hầu hết các phương pháp sản xuất lúa hiện nay đều sử dụng các hóa chất độc hại trong việc phòng trừ bệnh và côn trùng gây hại, nên dẫn đến ô nhiễm môi trường. Kích thích tính kháng lưu dẫn giúp kích hoạt cơ chế tự nhiên kháng bệnh của cây có thể là giải pháp bảo vệ thực vật thay thế an toàn với môi trường. Việc ứng dụng tiến bộ này vào trong sản xuất lúa có thể làm giảm lượng hóa chất sử dụng, đóng góp vào việc giảm thiểu ô nhiễm môi trường và sự phát triển của một nền nông nghiệp bền vững. Nghiên cứu đã được thực hiện tại nhà lưới trường Đại học Cần Thơ để tuyển chọn hóa chất và phương pháp sử dụng hóa chất để tăng cường sức khỏe giúp cây lúa vượt qua bệnh vàng lùn. Hóa chất kích kháng được sử dụng ở một nồng độ rất thấp (đơn vị là mM). Kết quả cho thấy chiều cao cây lúa khi xử lý chất kích kháng tốt hơn so đối chứng không xử lý. Bên cạnh đó, số cây lúa nhiễm bệnh giảm, tỉ lệ hạt chắc và năng suất tăng khi cây lúa được xử lý với chất kích kháng. Trong số các chất kích kháng đã sử dụng, acid oxalic cho hiệu quả vượt trội. Với chất acid oxalic, phương pháp ngâm hạt cho hiệu quả kích kháng tốt hơn phương pháp áo hạt

Transformer-Based Deep Learning Detector for Dual-Mode Index Modulation 3D-OFDM

In this paper, we propose a deep learning-based signal detector called TransD3D-IM, which employs the Transformer framework for signal detection in the Dual-mode index modulation-aided three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM) system. In this system, the data bits are conveyed using dual-mode 3D constellation symbols and active subcarrier indices. As a result, this method exhibits significantly higher transmission reliability than current IM-based models with traditional maximum likelihood (ML) detection. Nevertheless, the ML detector suffers from high computational complexity, particularly when the parameters of the system are large. Even the complexity of the Log-Likelihood Ratio algorithm, known as a low-complexity detector for signal detection in the DM-IM-3D-OFDM system, is also not impressive enough. To overcome this limitation, our proposal applies a deep neural network at the receiver, utilizing the Transformer framework for signal detection of DM-IM-3D-OFDM system in Rayleigh fading channel. Simulation results demonstrate that our detector attains to approach performance compared to the model-based receiver. Furthermore, TransD3D-IM exhibits more robustness than the existing deep learning-based detector while considerably reducing runtime complexity in comparison with the benchmarks

Out-of-the-loop information hiding for HEVC video

Communication using internet and digital media is more and more popular. Therefore, the security and privacy of data transmission are highly demanded. One effective technique providing this requirement is information hiding. This technique allows to conceal secret information into a video file, an audio, or a picture. In this paper, we propose a low complexity out-of-the-loop information hiding algorithm for a video pre-encoded with the high efficiency video coding standard. Only selected components such as the motion vector difference and transform coefficients of the video are extracted and modified, bypassing the need of fully decoding and re-encoding the video. In order to reduce the propagation error caused by hiding information, the dependency between video frames is taken into account when distributing the information over the frame. Several embedding strategies are investigated. The experimental results show that the information should be hidden in smaller blocks to reduce quality loss. Using a smart distribution of information across the frames can keep the quality loss under 1 dB PSNR for an information payload of 15 kbps. When such a strategy is used, embedding information in the transform coefficients only slightly outperforms the modification of motion vector differences

CAMELLIA HOABINHENSIS (THEACEAE: SECT. CHRYSANTHA), A NEW YELLOW-FLOWERED SPECIES FROM NORTHERN VIETNAM

A new species, Camellia hoabinhensis (section Chrysantha, Theaceae), is described and illustrated based on specimens collected from a lowland forest on limestone hills in Hoa Binh Province, northern Vietnam. The species is characterized by its small habit to 4.5 m tall, large flowers 9.0–9.5 cm in diameter with 18–19 light yellow petals, and hairy filaments, ovaries, and styles. A comparison of the new species with similar species, C. euphlebia, C. impressinervis, C. phanii, and C. velutina, is provided. The IUCN conservation status of the proposed species is Data Deficient (DD)

Efficient bit rate transcoding for high efficiency video coding

High efficiency video coding (HEVC) shows a significant advance in compression efficiency and is considered to be the successor of H.264/AVC. To incorporate the HEVC standard into real-life network applications and a diversity of other applications, efficient bit rate adaptation (transrating) algorithms are required. A current problem of transrating for HEVC is the high computational complexity associated with the encoder part of such a cascaded pixel domain transcoder. This paper focuses on deriving an optimal strategy for reducing the transcoding complexity with a complexity-scalable scheme. We propose different transcoding techniques which are able to reduce the transcoding complexity in both CU and PU optimization levels. At the CU level, CUs can be evaluated in top-to-bottom or bottom-to-top flows, in which the coding information of the input video stream is utilized to reduce the number of evaluations or to early terminate certain evaluations. At the PU level, the PU candidates are adaptively selected based on the probability of PU sizes and the co-located input PU partitioning. Moreover, with the use of different proposed methods, a complexity-scalable transrating scheme can be achieved. Furthermore, the transcoding complexity can be effectively controlled by the machine learning based approach. Simulations show that the proposed techniques provide a superior transcoding performance compared to the state-of-the-art related works. Additionally, the proposed methods can achieve a range of trade-offs between transrating complexity and coding performance. From the proposed schemes, the fastest approach is able to reduce the complexity by 82% while keeping the bitrate loss below 3%