This paper describes a novel low-latency 4K 60 fps HEVC (high efficiency video coding)/H.265 multi-channel encoding system with content-aware bitrate control for live streaming. Adaptive bitrate (ABR) streaming techniques, such as MPEG-DASH (dynamic adaptive streaming over HTTP) and HLS (HTTP live streaming), spread widely on Internet video streaming. Live content has increased with the expansion of streaming services, which has led to demands for traffic reduction and low latency. To reduce network traffic, we propose content-aware dynamic and seamless bitrate control that supports multi-channel real-time encoding for ABR, including 4K 60 fps video. Our method further supports chunked packaging transfer to provide low-latency streaming. We adopt a hybrid architecture consisting of hardware and software processing. The system consists of multiple 4K HEVC encoder LSIs that each LSI can encode 4K 60 fps or up to high-definition (HD) ×4 videos efficiently with the proposed bitrate control method. The software takes the packaging process according to the various streaming protocol. Experimental results indicate that our method reduces encoding bitrates obtained with constant bitrate encoding by as much as 56.7%, and the streaming latency over MPEG-DASH is 1.77 seconds.

For formulating Quadratic Knapsack Problems (QKPs) into the form of Quadratic Unconstrained Binary Optimization (QUBO), it is necessary to introduce an integer variable, which converts and incorporates the knapsack capacity constraint into the overall energy function. In QUBO, this integer variable is encoded with auxiliary binary variables, and the encoding method used for it affects the behavior of Simulated Annealing (SA) significantly. For improving the efficiency of SA for QKP instances, this paper first visualized and analyzed their annealing processes encoded by conventional binary and unary encoding methods. Based on this analysis, we proposed a novel hybrid encoding (HE), getting the best of both worlds. The proposed HE obtained feasible solutions in the evaluation, outperforming the others in small- and medium-scale models.

Order-preserving encryption using the hypergeomatric probability distribution leaks about the half bits of a plaintext and the distance between two arbitrary plaintexts. To solve these problems, Popa et al. proposed a mutable order-preserving encoding. This is a keyless encoding scheme that adopts an order-preserving index locating the corresponding ciphertext via tree-based data structures. Unfortunately, it has the following shortcomings. First, the frequency of the ciphertexts reveals that of the plaintexts. Second, the indices are highly correlated to the corresponding plaintexts. For these reasons, statistical cryptanalysis may identify the encrypted fields using public information. To overcome these limitations, we propose a multi-tree approach to the mutable order-preserving encoding. The cost of interactions increases by the increased number of trees, but the proposed scheme mitigates the distribution leakage of plaintexts and also reduces the problematic correlation to plaintexts.

This paper proposes and characterizes an A/D converter (ADC) based on a spiking neuron model with a rectangular threshold signal. The neuron repeats an integrate-and-fire process and outputs a superstable spike sequence. The dynamics of this system are closely related to those of rate-encoding ADCs. We propose an ADC system based on the spiking neuron model. We derive a theoretical parameter region in a limited time interval of the digital output sequence. We analyze the conversion characteristics in this region and verify that they retain the monotonic increase and rate encoding of an ADC.

Maximum run-length limited codes are constraint codes used in communication and data storage systems. Insertion/deletion correcting codes correct insertion or deletion errors caused in transmitted sequences and are used for combating synchronization errors. This paper investigates the maximum run-length limited single insertion/deletion correcting (RLL-SIDC) codes. More precisely, we construct efficiently encodable and decodable RLL-SIDC codes. Moreover, we present its encoding and decoding algorithms and show the redundancy of the code.

Crowd flow prediction in high density urban scenes is involved in a wide range of intelligent transportation and smart city applications, and it has become a significant topic in urban computing. In this letter, a CNN-based framework called Pyramidal Spatio-Temporal Network (PSTNet) for crowd flow prediction is proposed. Spatial encoding is employed for spatial representation of external factors, while prior pyramid enhances feature dependence of spatial scale distances and temporal spans, after that, post pyramid is proposed to fuse the heterogeneous spatio-temporal features of multiple scales. Experimental results based on TaxiBJ and MobileBJ demonstrate that proposed PSTNet outperforms the state-of-the-art methods.

This paper constructs packet-oriented erasure correcting codes and their systematic forms for the distributed storage systems. The proposed codes are encoded by exclusive OR and bit-level shift operation. By the shift operation, the encoded packets are slightly longer than the source packets. This paper evaluates the extra length of the encoded packets, called overhead, and shows that the proposed codes have smaller overheads than the zigzag decodable codes, which are existing codes using bit-level shift operation and exclusive OR.

Bit-interleaved coded modulation with iterative decoding (BICM-ID) is suitable for correlated Rayleigh fading channels. Additionally, BICM-ID using differential encoding can avoid the pilot overhead. In this paper, we consider BICM-ID using 16-DAPSK (differential amplitude and phase-shift keying). We first derive the probability of receiving signals conditioned on the transmission of input bits for general differential encoding; then we propose two new 16-DAPSK bit labeling methods. In addition, convolutional codes for the new bit labeling are developed. Both the minimum distance and the simulation results show that the proposed labeling has better error performance than that of the original differential encoding, and the searched new codes can further improve the error performance.

Attribute-based encryption (ABE), a cryptographic primitive, realizes fine-grained access control. Because of its attractive functionality, many systems based on ABE have been constructed to date. In such cryptographic systems, revocation functionality is indispensable to handle withdrawal of users, secret key exposure, and others. Although many ABE schemes with various functionalities have been proposed, only a few of these are revocable ABE (RABE). In this paper, we propose two generic constructions of RABE from ABE. Our first construction employs the pair encoding framework (Attrapadung, EUROCRYPT 2014), and combines identity-based revocation and ABE via the generic conjunctive conversion of Attrapadung and Yamada (CT-RSA 2015). Our second construction converts ABE to RABE directly when ABE supports Boolean formulae. Because our constructions preserve functionalities of the underlying ABE, we can instantiate various fully secure RABE schemes for the first time, e.g., supporting regular languages, with unbounded attribute size and policy structure, and with constant-size ciphertext and secret key.

8B/10B is an encoding technique largely used in different communication protocols, with several advantages such as zero DC bias. In the last years transmission rates have grown rapidly, thus the need of encoders with better performance in terms of throughput, area and power consumption raised rapidly. In this article we will present and discuss the architecture of two symbols parallel encoder, comparing it with a classical pipelined solution.

In this paper, we propose a novel self-supervised learning of video representation which is capable to anticipate the video category by only reading its short clip. The key idea is that we employ the Siamese convolutional network to model the self-supervised feature learning as two different image matching problems. By using frame encoding, the proposed video representation could be extracted from different temporal scales. We refine the training process via a motion-based temporal segmentation strategy. The learned representations for videos can be not only applied to action anticipation, but also to action recognition. We verify the effectiveness of the proposed approach on both action anticipation and action recognition using two datasets namely UCF101 and HMDB51. The experiments show that we can achieve comparable results with the state-of-the-art self-supervised learning methods on both tasks.

In mobile and wireless networks, controlling data delivery latency is one of open problems due to the stochastic nature of wireless channels, which are inherently unreliable. This paper explores how the current best-effort throughput-oriented wireless services might evolve into latency-sensitive enablers of new mobile applications such as remote three-dimensional (3D) graphical rendering for interactive virtual/augmented-reality overlay. Assuming that the signal propagation delay and achievable throughput meet the standard latency requirements of the user application, we examine the idea of trading excess/federated bandwidth for the elimination of non-negligible delay of data re-ordering, caused by temporal transmission failures and buffer overflows. The general system design is based on (i) spatially diverse data delivery over multiple paths with uncorrelated outage likelihoods; and (ii) forward packet-loss protection (FPP), creating encoding redundancy for proactive recovery of intolerably delayed data without end-to-end retransmissions. Analysis and evaluation are based on traces of real life traffic, which is measured in live carrier-grade long term evolution (LTE) networks and campus WiFi networks, due to no such system/environment yet to verify the importance of spatial diversity and encoding redundancy. Analysis and evaluation reveal the seriousness of the latency problem and that the proposed FPP with spatial diversity and encoding redundancy can minimize the delay of re-ordering. Moreover, a novel FPP effectiveness coefficient is proposed to explicitly represent the effectiveness of EPP implementation.

High dynamic range (HDR) images contain more details of the scene as compared to commonly used low dynamic range (LDR) images. The additional information in the HDR images is important for applications such as high-quality graphics rendering, sensing, scene analysis, and surveillance etc. Moreover, HDR images would provide better visualization experience on HDR displays, which might become more common in near future. Therefore, it is important to encode the entire dynamic range of the HDR images. In this paper, a new lossless, four-channel, eight bits per channel, format for encoding floating-point HDR images is proposed. The format is similar to the well-known RGBE format but constructs the E channel differently for better accuracy. Experimental results show that our technique could reduce the rounding error of the RGBE by more than 88%. In addition, there was a reduction of 44.3% in average error for all 33 images in the database used for this study.

The development of multi-view video has paved the way for emerging 3D applications. In general multi-view video streaming, video frames for all viewpoints, i.e., cameras, must be transmitted to viewers because the view-switching demands of all viewers are unpredictable. However, existing transmission schemes are highly vulnerable to frame loss. Specifically, frame loss in one viewpoint can induce a collapse in decoding for other viewpoints. To improve loss-resilience, this paper proposes a multi-path based multi-view video transmission scheme. Our scheme encodes video frames into multiple versions that are independent of each other, using inter-view prediction. The scheme then transmits each version using multiple transmission paths. Our scheme makes three contributions: 1) it reduces video traffic even for a large number of cameras, 2) it prevents an increase in the number of undecoded video frames caused by single-frame loss, and 3) it conceals frame loss by taking video frames from other paths. Evaluations show that our proposed scheme improves video quality by 3 dB, as compared to existing transmission schemes in loss-prone environments.

In order to prevent the synonym substitution breaking the balance among frequencies of synonyms and improve the statistical undetectability, this paper proposed a novel linguistic steganography based on synonym run-length encoding. Firstly, taking the relative word frequency into account, the synonyms appeared in the text are digitized into binary values and expressed in the form of runs. Then, message are embedded into the parities of runs' lengths by self-adaptively making a positive or negative synonym transformation on boundary elements of two adjacent runs, while preserving the number of relative high and low frequency synonyms to reduce the embedding distortion. Experimental results have shown that the proposed synonym run-length encoding based linguistic steganographic algorithm makes fewer changes on the statistical characteristics of cover texts than other algorithms, and enhances the capability of anti-steganalysis.

High Efficiency Video Coding (HEVC/H.265) obtains 50% bit rate reduction than H.264/AVC standard with comparable quality at the cost of high computational complexity. Merge mode is one of the most important new features introduced in HEVC's inter prediction. Merge mode and traditional inter mode consume about 90% of the total encoding time. To address this high complexity, this paper utilizes the merge mode to accelerate inter prediction by four strategies. 1) A merge candidate decision is proposed by the sum of absolute transformed difference (SATD) cost. 2) An early merge termination is presented with more than 90% accuracy. 3) Due to the compensation effect of merge candidates, symmetric motion partition (SMP) mode is disabled for non-8×8 coding units (CUs). 4) A fast coding unit filtering strategy is proposed to reduce the number of CUs which need to be fine-processed. Experimental results demonstrate that our fast strategies can achieve 35.4%-58.7% time reduction with 0.68%-1.96% BD-rate increment in RA case. Compared with similar works, the proposed strategies are not only among the best performing in average-case complexity reduction, but also notably outperforming in the worst cases.

A lot of encryption and watermarking schemes have been developed as countermeasures to protect copyrights of broadcast or multicast content from malicious subscribers (traitors) that make pirate receivers (PRs) to use the content illegally. However, solo use of these schemes does not necessarily work well. Traitor tracing encryption schemes are a type of broadcasting encryption and have been developed for broadcasting and multicast services. There are multiple distinct decryption keys for each encryption key, and each service subscriber is given a unique decryption key. Any subscriber that redistributes his or her decryption key to a third party or who uses it and maybe other keys to make a PR can be identified with using the tracing algorithm of the scheme that is used by the services. However, almost all previous schemes have the same weakness; that is, they are vulnerable to an attack (content comparison attack). This is a concrete example such that solo use of the scheme does not work well. The attack involves multiple distinct decryption keys and a content-data comparison mechanism. We have developed a method, called complementary traitor tracing method (CTT), that makes traitor tracing schemes secure against content comparison attacks. It makes it impossible for PRs to distinguish ordinary content data from test data and makes traitor tracing schemes effective against all PRs, even those with multiple distinct decryption keys. CTT is made with a simple combination of schemes that are absolutely necessary. It makes broadcasting or multicast services secure.

In this work, a high efficiency coding unit (CU) size decision algorithm is proposed for high efficiency video coding (HEVC) inter coding. The CU splitting or non-splitting is modeled as a binary classification problem based on probability graphical model (PGM). This method incorporates two sub-methods: CU size termination decision and CU size skip decision. This method focuses on the trade-off between encoding efficiency and encoding complexity, and it has a good performance. Particularly in the high resolution application, simulation results demonstrate that the proposed algorithm can reduce encoding time by 53.62%-57.54%, while the increased BD-rate are only 1.27%-1.65%, compared to the HEVC software model.

Local spatio-temporal features are popular in the human action recognition task. In practice, they are usually coupled with a feature encoding approach, which helps to obtain the video-level vector representations that can be used in learning and recognition. In this paper, we present an efficient local feature encoding approach, which is called Approximate Sparse Coding (ASC). ASC computes the sparse codes for a large collection of prototype local feature descriptors in the off-line learning phase using Sparse Coding (SC) and look up the nearest prototype's precomputed sparse code for each to-be-encoded local feature in the encoding phase using Approximate Nearest Neighbour (ANN) search. It shares the low dimensionality of SC and the high speed of ANN, which are both desired properties for a local feature encoding approach. ASC has been excessively evaluated on the KTH dataset and the HMDB51 dataset. We confirmed that it is able to encode large quantity of local video features into discriminative low dimensional representations efficiently.

In this paper, we present a new encoding/decoding method for dynamic multidimensional datasets and its implementation scheme. Our method encodes an n-dimensional tuple into a pair of scalar values even if n is sufficiently large. The method also encodes and decodes tuples using only shift and and/or register instructions. One of the most serious problems in multidimensional array based tuple encoding is that the size of an encoded result may often exceed the machine word size for large-scale tuple sets. This problem is efficiently resolved in our scheme. We confirmed the advantages of our scheme by analytical and experimental evaluations. The experimental evaluations were conducted to compare our constructed prototype system with other systems; (1) a system based on a similar encoding scheme called history-offset encoding, and (2) PostgreSQL RDBMS. In most cases, both the storage and retrieval costs of our system significantly outperformed those of the other systems.