In video-text retrieval task, mainstream framework consists of three parts: video encoder, text encoder and similarity calculation. MMT (Multi-modal Transformer) achieves remarkable performance for this task, however, it faces the problem of insufficient training dataset. In this paper, an efficient multimodal aggregation network for video-text retrieval is proposed. Different from the prior work using MMT to fuse video features, the NetVLAD is introduced in the proposed network. It has fewer parameters and is feasible for training with small datasets. In addition, since the function of CLIP (Contrastive Language-Image Pre-training) can be considered as learning language models from visual supervision, it is introduced as text encoder in the proposed network to avoid overfitting. Meanwhile, in order to make full use of the pre-training model, a two-step training scheme is designed. Experiments show that the proposed model achieves competitive results compared with the latest work.

4K/8K satellite broadcasting featuring ultra-high definition video and sound was launched in Japan in 2018. This is the first 8K ultra high definition television (UHDTV) broadcasting in the world, with 16 times as many pixels as HDTV and 3D sound with 22.2ch audio. The large amount of information that has to be transmitted means that a new satellite broadcasting transmission system had to be developed. In this paper, we describe this transmission system, focusing on the technology that enables 4K/8K UHDTV satellite broadcasting.

This paper presents a novel TV event detection method for automatically generating TV program digests by using Twitter data. Previous studies of TV program digest generation based on Twitter data have developed TV event detection methods that analyze the frequency time series of tweets that users made while watching a given TV program; however, in most of the previous studies, differences in how Twitter is used, e.g., sharing information versus conversing, have not been taken into consideration. Since these different types of Twitter data are lumped together into one category, it is difficult to detect highlight scenes of TV programs and correctly extract their content from the Twitter data. Therefore, this paper presents a highlight scene detection method to automatically generate TV program digests for TV programs based on Twitter data classified by Twitter user behavior. To confirm the effectiveness of the proposed method, experiments using 49 soccer game TV programs were conducted.

Vehicular Ad hoc Network (VANET) is an important part of the Intelligent Transportation System (ITS). VANETs can realize communication between moving vehicles, infrastructures and other intelligent mobile terminals, which can greatly improve the road safety and traffic efficiency effectively. Existing studies of vehicular ad hoc network usually consider only one data transmission model, while the increasing density of traffic data sources means that the vehicular ad hoc network is evolving into Heterogeneous Vehicular Network (HetVNET) which needs hybrid data transmission scheme. Considering the Heterogeneous Vehicular Network, this paper presents a hybrid transmission MAC protocol including vehicle to vehicle communication (V2V) and vehicle to infrastructure communication (V2I/I2V). In this protocol, the data are identified according to timeliness, on the base of the traditional V2V and V2I/I2V communication. If the time-sensitive data (V2V data) fail in transmission, the node transmits the data to the base station and let the base station cooperatively transmit the data with higher priority. This transmission scheme uses the large transmission range of base station in an effective manner. In this paper, the queueing models of the vehicles and base station are analyzed respectively by one-dimensional and two-dimensional Markov Chain, and the expressions of throughput, packet drop rate and delay are also derived. The simulation results show that this MAC protocol can improve the transmission efficiency of V2V communication and reduce the delay of V2V data without losing the system performance.

Recently, P2PTV is a popular application to deliver video streaming data over the Internet. On the overlay network, P2PTV applications create logical links between pairs of peers considering round trip time (RTT) without physical network consideration. P2PTV packets are shared over a network without localization awareness which is a serious problem for Internet Service Providers (ISPs). A delay-insertion-based traffic localization scheme was proposed for solving this problem. However, this scheme sometimes leads the newly joining peer to download streaming traffic from a local neighbor peer which has only scarce upload bandwidth. This paper proposes a novel scheme of delay-insertion-based traffic localization in which the router estimates relay capability to each relay peer candidate and leads the newly joining peer to connect to a neighbor peer with sufficient performance for relaying video data. Parameters were evaluated for the optimized condition in the relay capability estimation process. In addition, experiments conducted on a real network show that our proposed scheme can prevent the newly joining peer from downloading video data from peers with insufficient relay capability and maintain video quality close to normal in a P2PTV system while ensuring efficient traffic localization at the level of the Autonomous System (AS) network.

We propose a method of interframe prediction in depth map coding that uses pixel-wise 3D motion estimated from encoded textures and depth maps. By using the 3D motion, an approximation of the depth map frame to be encoded is generated and used as a reference frame of block-wise motion compensation.

8K Ultra High Definition Television (UHDTV) requires extremely high throughput for video decoding based on H.265. In H.265, intra coding could significantly enhance video compression efficiency, at the expense of an increased computational complexity compared with H.264. For intra prediction of 8K UHDTV real-time H.265 decoding, the joint complexity and throughput issue is more difficult to solve. Therefore, based on the divide-and-conquer strategy, we propose a new VLSI architecture in this paper, including two techniques, in order to achieve 8K UHDTV H.265 intra prediction decoding. The first technique is the LUT based Reference Sample Fetching Scheme (LUT-RSFS), reducing the number of reference samples in the worst case from 99 to 13. It further reduces the circuit area and enhances the performance. The second one is the Hybrid Block Reordering and Data Forwarding (HBRDF), minimizing the idle time and eliminating the dependency between TUs by creating 3 Data Forwarding paths. It achieves the hardware utilization of 94%. Our design is synthesized using Synopsys Design Compiler in 40nm process technology. It achieves an operation frequency of 260MHz, with a gate count of 217.8K for 8-bit design, and 251.1K for 10-bit design. The proposed VLSI architecture can support 4320p@120fps H.265 intra decoding (8-bit or 10-bit), with all 35 intra prediction modes and prediction unit sizes ranging from 4×4 to 64×64.

This paper presents an Adapting Block-Propagative Background Subtraction (ABPBGS) designed for Ultra High Definition Television (UHDTV) foreground detection. The main idea is to detect block after block along the objects in order to skip all areas of the image in which there is no moving object. This is particularly interesting for UHDTV when the objects of interest could represent not even 0.1% of the total area. From a seed block which is determined in a previous iteration, the detection will spread along an object as long as it detects a part of that object. A block history map guaranties that each block is processed only once. Moreover, only small blocks are loaded and processed, thus saving computational time and memory usage. The process of each block is independent enough to be easily parallelized. Compared to 9 state-of-the-art works, the ABPBGS achieved the best results with an average global quality score of 0.57 (1 being the maximum) on a dataset of 4K and 8K UHDTV sequences developed for this work. None of the state-of-the-art methods could process 4K videos in reasonable time while the ABPBGS has shown an average speed of 5.18fps. In comparison, 5 of the 9 state-of-the-art methods performed slower on 270p down-scale version of the same videos. The experiments have also shown that for the process an 8K UHDTV video the ABPBGS can divide the memory required by about 24 for a total of 450MB.

Most P2PTV systems select a neighbor peer in an overlay network using RTT or a random method without considering the underlying network. Streaming traffic is shared over a network without localization awareness, which is a serious problem for Internet Service Providers. In this paper, we present a novel scheme to achieve P2PTV traffic localization by inserting delay into P2P streaming packets, so that the length of the inserted delay depends on the AS hop distance between a peer and its neighbor peer. Experiments conducted on a real network show that our proposed scheme can perform efficient traffic localization.

Viterbi decoding is commonly used for several protocols, but computational cost is quite high and thus it is necessary to implement it effectively. This paper describes GPU implementation of Viterbi decoder utilizing three-point Viterbi decoding algorithm (TVDA), in which the received bits are divided into multiple chunks and several chunks are decoded simultaneously. Coalesced access and Warp Shuffle, which is new instruction introduced are also utilized in order to improve decoder performance. In addition, iterative execution of parallel chunks decoding reduces the latency of proposed Viterbi decoder in order to utilize the decoder as a part of GPU-based SDR transceiver. As the result, the throughput of proposed Viterbi decoder is improved by 23.1%.

Recently, standard organizations of ATSC, DVB and TTA have been working to design various immersive media broadcasting services such as the hybrid network-based 3D video, UHD video and multiple views. This letter focuses on providing a new synchronization and transport system target decoder (T-STD) model of 3D video distribution based on heterogeneous transmission protocol in a hybrid network environment, where a broadcasting network and broadband (IP) network are combined. On the basis of the experimental results, the proposed technology has been proved to be successfully used as a core element for synchronization and T-STD model in a hybrid network-based 3D broadcasting. It has been also found out that it could be used as a base technique for various IP associated hybrid broadcasting services.

In this paper, we propose the novel concept of a spectrum database for improving the efficiency of spectrum utilization. In the current design of TV white space spectrum databases, a propagation model is utilized to determine the spectrum availability. However, this propagation model has poor accuracy for radio environment estimation because it requires a large interference margin for the PU coverage area to ensure protection of primary users (PUs); thus, it decreases the spectrum sharing efficiency. The proposed spectrum database consists of radio environment measurement results from sensors on mobile terminals such as vehicles and smart phones. In the proposed database, actual measurements of radio signals are used to estimate radio information regarding PUs. Because the sensors on mobile terminals can gather a large amount of data, accurate propagation information can be obtained, including information regarding propagation loss and shadowing. In this paper, we first introduce the architecture of the proposed spectrum database. Then, we present experimental results for the database construction using actual TV broadcast signals. Additionally, from the evaluation results, we discuss the extent to which the proposed database can mitigate the excess interference margin.

We propose simple but efficient encapsulation architecture. In the architecture, clients can better decode Extensible Markup Language (XML) based service information for TV contents with schema digest. Our experimental results show the superiority of the proposed architecture by comparing the compression ratios and decoding times of the proposed architecture and the existing architectures.

In this paper, VLSI architecture design of unified motion vector (MV) and boundary strength (BS) parameter decoder (PDec) for 8K UHDTV HEVC decoder is presented. The adoption of new coding tools in PDec, such as Advanced Motion Vector Prediction (AMVP), increases the VLSI hardware realization overhead and memory bandwidth requirement, especially for 8K UHDTV application. We propose four techniques for these challenges. Firstly, this work unifies MV and BS parameter decoders for line buffer memory sharing. Secondly, to support high throughput, we propose the top-level CU-adaptive pipeline scheme by trading off between implementation complexity and performance. Thirdly, PDec process engine with optimizations is adopted for 43.2k area reduction. Finally, PU-based coding scheme is proposed for 30% DRAM bandwidth reduction. In 90nm process, our design costs 93.3k logic gates with 23.0kB line buffer. The proposed architecture can support real-time decoding for 7680x4320@60fps application at 249MHz in the worst case.

TV white space (TVWS) brings potential opportunities to relieve the growing spectrum scarcity. Therefore organizations like the FCC have suggested the co-channel deployment of cellular networks (CNs) on condition that a keep-out distance from the protected region of TV receivers is maintained. However the consequent CN interference has not been described. In addition, considering the wide range of TV coverage, it is also inefficient and wasteful not applying the vacant spectra for secondary user (SU) communication by opportunistic access inside the TV coverage zone. In this paper, we first investigate the aggregate interference from CNs outside the protected area to find out how the interference is generated, and then research the available spectrum resource distribution for SUs inside the TV coverage zone under aggregate interference constraints to utilize TVWS more efficiently. Specifically, we model CN in three aspects. A close-form interference probability distribution function (PDF) is proposed. Since the PDF is too complex to analyze, we approximate it as Gaussian and prove the accuracy of our approximation with Kolmogorov-Smirnov test. Then, available spectra maximization is formulated as an optimization problem under both TV and SU receiver outage probability constraints. We find that available spectra demonstrate a volcano-shaped geographical distribution and optimal network-status-aware SU transmit power exists to maximize the spectra. Our analysis reveals the characteristics of interference in TVWS and contributes to the utilization improvement of white space.

In measuring TV ratings, some features can be significant at a certain time, whereas they can be meaningless in other time periods. Because the importance of features can change, a model capturing the time changing relevance is required in order to estimate TV ratings more accurately. Therefore, we focus on the time-awareness of features, particularly the time when the words of tweets are used. We develop a correlation-based, time-aware feature selection algorithm which finds the optimal time period of each feature, and the estimation method using e-SVR based on top-n-features that are ordered by correlation. We identify that the correlation values between features and TV ratings vary according to the time of postings - before and after the broadcast time. This implies that the relevance of features can change according to the time of the tweets. Experimental results indicate that the proposed method has better performance compared with the method based on count-based features. This result implies that understanding the time-dependency of features can be helpful in improving the accuracy of measuring TV ratings.

In the digital terrestrial TV broadcasting system, it is important to evaluate both quantitative levels and sources of overreach interference, because it can degrade the TV service quality. This paper newly proposes an overreach measurement method that simultaneously monitors RSSI (Received Signal Strength Indicator) and CNR (Carrier to Noise power Ratio) of the TV waves and RSSI of FM waves. The results of measurements conducted in Hiroshima prefecture show that our proposed method can evaluate the level of overreach interference in the TV waves and also identify the source of the interference. Total 43 overreach interference events were found in the proposed method from one-year measurement in 2012. Based on M profile data, this paper also shows that the main factor of the overreach interference in this measurement is duct propagation due to meteorological condition.

This paper proposes out-of-band emission reduction schemes for IEEE 802.11af based Wireless Local Area Network (WLAN) systems operating in TV White Spaces (TVWS). IEEE 802.11af adopts Orthogonal Frequency Division Multiplexing (OFDM) to exploit the TVWS spectrum effectively. The combination of the OFDM and TVWS may be able to solve the problem of frequency depletion. However the TVWS transmitter must satisfy a strict transmission spectrum mask and reduce out-of-band emission to protect the primary users. The digital convolution filter is one way of reducing the out-of-band emission. Unfortunately, implementing a strict mask needs a large number of filter taps, which causes high implementation complexity. Time-domain windowing is another effective approach. This scheme reduces out-of-band emission with low complexity but at the price of shortening the effective guard interval. This paper proposes a mechanism that jointly uses these two schemes for out-of-band emission reduction. Moreover, the appropriate windowing duration design is proposed in terms of both the out-of-band emission suppression and throughput performance for all mandatory mode of IEEE 802.11af system. The proposed time-domain windowing design reduces the number of multiplier by 96.5%.

Demand for wireless communication is increasing significantly, but the frequency resources available for wireless communication are quite limited. Currently, various countries are prompting the use of TV white spaces (TVWS). IEEE 802.11 Working Group (WG) has started a Task Group (TG), namely IEEE 802.11af, to develop an international standard for Wireless local Area Networks (WLANs) in TVWS. In order to increase maximum throughput, a channel aggregation mechanism is introduced in the draft standard. In Japan, ISDB-T based area-one-segment broadcasting system (Area-1seg) which is a digital TV broadcast service in limited areas has been permitted to offer actual TVWS services since April 2012. The operation of the IEEE 802.11af system shall not jeopardize the Area-1seg system due to the common operating frequency band. If the Area-1seg partially overlaps with the IEEE 802.11af in some frequency, the IEEE 802.11af cannot use the channel aggregation mechanism due to a lack of channels. As a result, the throughput of the IEEE 802.11af deteriorates. In this paper, the physical layer of IEEE 802.11af D4.0 is introduced briefly, and a partial subcarrier system for IEEE 802.11af is proposed to efficiently use the TVWS spectrum. The IEEE 802.11af co-exist with the Area-1seg by using null subcarriers. Computer simulation shows up to around 70% throughput gain is achieved with the proposed mechanism.

Moving objects or more generally foreground objects are the simplest objects in the field of computer vision after the pixel. Indeed, a moving object can be defined by 4 integers only, either two pairs of coordinates or a pair of coordinates and the size. In fixed camera scenes, moving objects (or blobs) can be extracted quite easily but the methods to produce them are not able to tell if a blob corresponds to remaining background noise, a single target or if there is an occlusion between many target which are too close together thus creating a single blob resulting from the fusion of all targets. In this paper we propose an novel method to refine moving object detection results in order to get as many blobs as targets on the scene by using a tracking system for additional information. Knowing if a blob is at proximity of a tracker allows us to remove noise blobs, keep the rest and handle occlusions when there are more than one tracker on a blob. The results show that the refinement is an efficient tool to sort good blobs from noise blobs and accurate enough to perform a tracking based on moving objects. The tracking process is a resolution free system able to reach speed such as 20 000fps even for UHDTV sequences. The refinement process itself is in real time, running at more than 2000fps in difficult situations. Different tests are presented to show the efficiency of the noise removal and the reality of the independence of the refinement tracking system from the resolution of the videos.