Entropy coding is an integral part of all modern hybrid block-based video codecs. The context adaptive binary arithmetic coding (CABAC) is a normative part of ITU-T/ISO/IEC video coding standards (H.264/AVC, HEVC and VVC), while the Alliance for Open Media (AOMedia) video standard (AV1) utilizes a context adaptive multi-symbol version of it for entropy coding. Recent explorations of new coding tools beyond AV1 capabilities have led to the improvement of the context adaptive multi-symbol arithmetic entropy coder. In this study, improvements to the context initialization process of the entropy coder are discussed in detail and results reported. The improvements include a) optimal selection of reference frame pairs and b) context modeling from selected reference frame pairs for tile context initialization. Two variants of the proposed method, Variant1 and Variant2 are implemented on top of the reference codebase (research-v8.0.0). Experimental results with CTCv7 show that for random access (RA) configuration, average BDRATE (PSNR-YUV) gains of -0.13% and -0.18% are achievable for Variant1 and Variant2 respectively. Meanwhile, for low delay (LD) configuration the reported gains are -0.47% and -0.50% respectively for Variant1 and Variant2.

Bluetooth Low Energy (BLE) backscatter is a promising candidate for battery-free Internet of Things (IoT) applications. We propose PassiveBLE, a backscatter system that can establish authentic and fully compatible BLE connections on data channels. The key enabling techniques include (i) a synchronization circuit that can wake up tags and activate backscatter communications with symbol-level accuracy to facilitate BLE data packet generation; (ii) a distributed coding scheme that offloads the major encoding and processing burdens from tags to the excitation source while achieving high throughput; (iii) a BLE connection scheduler to enable fully compatible BLE connection interactions, including connection establishment, maintenance and termination for multiple backscatter tags. We prototype PassiveBLE tags with off-the-shelf components and also conduct comprehensive experiments to evaluate the performance. Results demonstrate that PassiveBLE achieves a success rate of over 99.9% in establishing commodity BLE connections. PassiveBLE also achieves commodity-compatible BLE communication with a high goodput of up to 974 kbps in LE 2M PHY mode and 532 kbps in LE 1M PHY mode, which is about 63.3× higher than existing commodity-level BLE backscatter system in the same mode.

Intra Block Copy (IntraBC) is a key coding tool for screen content video, enabling blocks to reference previously reconstructed regions within the same frame. While IntraBC was introduced in AV1 and further enhanced in VVC, the AVM development framework offers new opportunities to improve its coding efficiency and hardware compatibility. This paper proposes three enhancements adopted into the AVM reference software: (1) a unified and extended local reference buffer design supporting multiple superblock sizes with fixed memory constraints; (2) a decoupled global-local search strategy that improves block vector prediction efficiency; and (3) the integration of intra Block-Adaptive Weighted Prediction (BAWP) into local IntraBC. Experimental results show significant screen content coding gains: -3.53%, -2.25%, -1.43% (YUV-PSNR) for all intra, random access and low delay, respectively, with minimal runtime overhead. These contributions have been adopted into the AVM standard and reference software.

Empowered by deep neural networks (DNNs), Wi-Fi fingerprinting has recently achieved astonishing localization performance to facilitate many security-critical applications in wireless networks, but it is inevitably exposed to adversarial attacks, where subtle perturbations can mislead DNNs to wrong predictions. Such vulnerability provides new security breaches to malicious devices for hampering wireless network security, such as malfunctioning geofencing or asset management. The prior adversarial attack on localization DNNs uses additive perturbations on channel state information (CSI) measurements, which is impractical in Wi-Fi transmissions. To transcend this limitation, this article presents FooLoc, which fools Wi-Fi CSI fingerprinting DNNs over the realistic wireless channel between the attacker and the victim access point (AP). We observe that though uplink CSIs are unknown to the attacker, the accessible downlink CSIs could be their reasonable substitutes at the same spot. We thoroughly investigate the multiplicative and repetitive properties of over-the-air perturbations and devise an efficient optimization problem to generate imperceptible yet robust adversarial perturbations. We implement FooLoc using commercial Wi-Fi APs and wireless open-access research platform (WARP) v3 boards in offline and online experiments, respectively. The experimental results show that FooLoc achieves overall attack success rates of about 70% in targeted attacks and above 90% in untargeted attacks with small perturbation-to-signal ratios of about −18 dB.

The Versatile Video Coding (VVC) standard adopts a more flexible partitioning structure beyond the High Efficiency Video Coding (HEVC) standard. The quadtree with nested multi-type tree (QTMT) partitioning structure greatly improves coding efficiency. Nevertheless, the typical recursive coding unit (CU) partitioning scheme causes a substantial increase in computational complexity at the encoder. In this paper, a unified fast algorithm is proposed for both intra and inter predictions, which makes use of various historical information of previously checked partitions. The proposed algorithm is implemented on top of the VVC reference software VTM-14.0. Experimental results show that the proposed algorithm achieves an average 42.47%, 40.46%, and 40.38% encoder runtime reduction with only 0.45%, 1.08%, and 1.18% Bjøntegaard delta bitrate loss (BDBR) in All Intra (AI), Random Access (RA), and Low Delay P (LDP) configurations, respectively.

The recent decade has witnessed an upsurge in the demands of intelligent and simplified Internet of Things (IoT) networks that provide ultra-low-power communication for numerous miniaturized devices. Although the research community has paid great attention to wireless protocol designs for these networks, researchers are handicapped by the lack of an energy-efficient software-defined radio (SDR) platform for fast implementation and experimental evaluation. Current SDRs perform well in battery-equipped systems, but fail to support miniaturized IoT devices with stringent hardware and power constraints. This paper takes the first step toward designing an ultra-low-power SDR that satisfies the ultra-low-power or even battery-free requirements of intelligent and simplified IoT networks. To achieve this goal, the core technique is the effective integration of µW-level backscatter in our SDR to sidestep power-hungry active radio frequency chains. We carefully develop a novel circuit design for efficient energy harvesting and power control, and devise a competent solution for eliminating the harmonic and mirror frequencies caused by backscatter hardware. We evaluate the proposed SDR using different modulation schemes, and it achieves a high data rate of 100 kb/s with power consumption less than 200 µW in the active mode and as low as 10 µW in the sleep mode. We also conduct a case study of railway inspection using our platform, achieving 1 kb/s battery-free data delivery to the monitoring unmanned aerial vehicle at a distance of 50 m in a real-world environment, and provide two case studies on smart factories and logistic distribution to explore the application of our platform.

Screen content coding (SCC) is developed to encode screen content videos, and it is an extension of High Efficiency Video Coding (HEVC). Since screen content videos contain computer-generated content that shows special characteristics, SCC adopts the new Intra Block Copy mode and Palette mode besides the HEVC based Intra mode to improve the coding efficiency. However, the exhaustive mode searching process makes the SCC encoder computational expensive. In this paper, a low-complexity intra prediction algorithm is proposed by the convolutional neural network (CNN). The proposed network skips unnecessary coding units (CUs) and mode candidates by imitating the behavior of the original SCC encoder. The network first decides if a CU size should be checked by analyzing global features, and it decides which mode should be checked by analyzing the local features. Experimental results show that the proposed algorithm achieves 53.44% computational complexity reduction on average with 1.94% Bjentegaard delta bitrate loss under All Intra configuration.

The screen content coding (SCC) extension of high efficiency video coding (HEVC) improves coding gain for screen content videos by introducing two new coding modes, namely, intra block copy (IBC) and palette (PLT) modes. However, the coding gain is achieved at the increased cost of computational complexity. In this paper, we propose a decision tree-based framework for fast intra mode decision by investigating various features in the training sets. To avoid the exhaustive mode searching process, a sequential arrangement of decision trees is proposed to check each mode separately by inserting a classifier before checking a mode. As compared with the previous approaches where both IBC and PLT modes are checked for screen content blocks (SCBs), the proposed coding framework is more flexible which facilitates either the IBC or PLT mode to be checked for SCBs such that computational complexity is further reduced. To enhance the accuracy of decision trees, dynamic features are introduced, which reveal the unique intermediate coding information of a coding unit (CU). Then, if all the modes are decided to be skipped for a CU at the last depth level, at least one possible mode is assigned by a CU-type decision tree. Furthermore, a decision tree constraint technique is developed to reduce the rate-distortion performance loss. Compared with the HEVC-SCC reference software SCM-8.3, the proposed algorithm reduces computational complexity by 47.62% on average with a negligible Bjøntegaard delta bitrate (BDBR) increase of 1.42% under all-intra (AI) configurations, which outperforms all the state-of-the-art algorithms in the literature.

Screen content coding (SCC) is an extension of high efficiency video coding (HEVC), and it is developed to improve the coding efficiency of screen content videos by adopting two new coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the flexible quadtree-based coding tree unit (CTU) partitioning structure and various mode candidates make the fast algorithms of the SCC extremely challenging. To efficiently reduce the computational complexity of SCC, we propose a deep learning-based fast prediction network DeepSCC that contains two parts: DeepSCC-I and DeepSCC-II. Before feeding to DeepSCC, incoming coding units (CUs) are divided into two categories: dynamic CTUs and stationary CTUs. For dynamic CTUs having different content as their collocated CTUs, DeepSCC-I takes raw sample values as the input to make fast predictions. For stationary CTUs having the same content as their collocated CTUs, DeepSCC-II additionally utilizes the optimal mode maps of the stationary CTU to further reduce the computational complexity. Compared with the HEVC-SCC reference software SCM-8.3, the proposed DeepSCC reduces the encoding time by 48.81% on average with a negligible Bjøntegaard delta bitrate increase of 1.18% under all-intra configuration.

Screen content coding (SCC) is an extension of high efficiency video coding by adopting new coding modes to improve the coding efficiency of SCC at the expense of increased complexity. This paper proposes an online-learning approach for fast mode decision and coding unit (CU) size decision in SCC. To make a fast mode decision, the corner point is first extracted as a unique feature in screen content, which is an essential pre-processing step to guide Bayesian decision modeling. Second, the distinct color number in a CU is derived as another unique feature in screen content to build the precise model using online-learning for skipping unnecessary modes. Third, the correlation of the modes among spatial neighboring CUs is analyzed to further eliminate unnecessary mode candidates. Finally, the Bayesian decision rule using online-learning is applied again to make a fast CU size decision. To ensure the accuracy of the Bayesian decision models, new scene change detection is designed to update the models. Results show that the proposed algorithm achieves 36.69% encoding time reduction with 1.08% Bjøntegaard delta bitrate (BDBR) increment under all intra configuration. By integrating into the existing fast SCC approach, the proposed algorithm reduces 48.83% encoding time with a 1.78% increase in BDBR.

Screen content coding (SCC) is the extension to high-efficiency video coding (HEVC) for compressing screen content videos. New coding tools, intrablock copy (IBC), and palette (PLT) modes, are introduced to encode screen content (SC) such as texts and graphics. The IBC mode is used for encoding repeating patterns by performing block matching within the same frame, while the PLT mode is designed for SC with few distinct colors by coding the major colors and their corresponding locations using an index map. However, the use of IBC and PLT modes increases the encoder complexity remarkably though coding efficiency can be improved. Therefore, we propose to have a mode skipping approach to reduce the encoder complexity of SCC by making use of SC characteristics, neighbor coding unit (CU) correlations, and intermediate cost information via random forest (RF). Detailed feature analyses and sample preparation are also described. A novel hyperparameter tuning approach with the consideration of coding bitrate and encoding time is proposed for RFs at each CU size to further boost the encoding process. Experimental results show that our proposed approach can obtain 45.06% average encoding time reduction with only a 1.08% increase in Bjøntegaard delta bitrate. Average encoding time can even be reduced to 58.57% by regulating the hyperparameters.

A screen content coding (SCC) extension to high efficiency video coding has been developed to incorporate many new coding tools in order to achieve better coding efficiency for videos mixed with camera-captured content and graphics/text/animation. For instance, the Intra Block Copy (IntraBC) mode helps to encode repeating patterns within the same frame while the Palette mode aims at encoding screen content with a few major colors. However, the IntraBC mode brings along high computational complexity due to the exhaustive block matching within the same frame though there are already some constraints and fast approaches applied to the IntraBC mode to reduce its complexity. Thus, we propose a fast intracoding scheme to reduce the complexity of using the IntraBC mode in SCC. Screen content always contains no sensor noise resulting in the characteristics with pixel exactness along both horizontal and vertical directions. These characteristics pave the way for mode skipping and early coding unit (CU) splitting. Besides, early CU pruning and early termination are proposed based on the rate distortion cost to further reduce encoder complexity. Moreover, we also propose reducing the complexity of the IntraBC mode by checking the hash value of each block candidate and the current block during block matching. With our proposed scheme, the encoding time is reduced compared with the SCC while the coding efficiency can still be maintained with a minor increase in the bjontegaard delta bitrate.

Screen Content Coding (SCC) is an extension of the High-Efficiency Video Coding (HEVC) for encoding screen content videos. However, there are many legacy screen content videos already encoded by HEVC. To efficiently migrate screen content videos from the existing HEVC to the emerging SCC, a machine learning based fast transcoding algorithm is proposed by using decision trees in this paper. To speed up the transcoding process, the intermediate data from both the HEVC decoder side and the SCC encoder side are jointly analyzed. Then the optimal coding unit (CU) sizes are mapped from HEVC to SCC while the mode candidates are adaptively checked according to the decision tree outcomes in the re-encoding process. Experimental results show that an average of 48.20% re-encoding time reduction is achieved with only 1.47% Bjontegaard delta bitrate loss using All Intra (AI) configuration.

Screen Content coding (SCC) is one of an extension to High Efficiency Video Coding (HEVC) developed by the Joint Collaborative Team on Video Coding (JCT-VC). It adopts two new coding tools, intra block copy (IBC) and palette (PLT) modes, to improve the compression performance for intra coding. Nevertheless, mode selection causes a substantial increase in encoding complexity. In this paper, a fast mode decision algorithm, which makes use of early mode skip decision based on the Bayesian decision rule using online learning, is proposed. The proposed algorithm is implemented in the SCC reference software SCM-7.0. Experimental results show that the proposed algorithm can achieve 23.2% complexity reduction on average with only 0.58% Bjontegaard delta bitrate loss in All Intra (AI) configurations.

Intra Block Copy (IBC) mode in a screen content coding (SCC) extension in High Efficiency Video Coding (HEVC) provides high coding gain by performing motion estimation (ME) and motion compensation (MC) to find the repetitive patterns within the same frame. Merge mode and Advanced Motion Vector Prediction (AMVP), which are originally used for inter mode, are also applied to the IBC mode. However, there are redundant coding bits when they are applied to IBC. Therefore, we propose decoder-side merge mode and AMVP for IBC in SCC so as to remove the redundancy. Experimental shows that the proposed method can achieve up to 0.25% Bjontegaard delta bitrate (BD-rate) reduction compared to the conventional SCC with negligible impact to encoding and decoding complexity.

Today’s data centers are designed with multi-core CPUs where multiple virtual machines (VMs) can be co-located into one physical machine or distribute multiple computing tasks onto one physical machine. The result is co-tenancy, resource sharing and competition. Modeling and predicting such co-run interference becomes crucial for job scheduling and Quality of Service assurance. Co-locating interference can be characterized into two components, sensitivity and pressure, where sensitivity characterizes how an application’s own performance is affected by a co-run application, and pressure characterizes how much contentiousness an application exerts/brings onto the memory subsystem. Previous studies show that with simple models, sensitivity and pressure can be accurately characterized for a single machine. We extend the models to consider cross-architecture sensitivity (across different machines).

Cloud computing has become a dominant computing paradigm to provide elastic, affordable computing resources to end users. Due to the increased computing power of modern machines powered by multi/many-core computing, data centers often co-locate multiple virtual machines (VMs) into one physical machine, resulting in co-tenancy, and resource sharing and competition. Applications or VMs co-locating in one physical machine can interfere with each other despite of the promise of performance isolation through virtualization. Modelling and predicting co-run interference therefore becomes critical for data center job scheduling and QoS (Quality of Service) assurance. Co-run interference can be categorized into two metrics, sensitivity and pressure, where the former denotes how an application's performance is affected by its co-run applications, and the latter measures how it impacts the performance of its co-run applications. This paper shows that sensitivity and pressure are both application-and architecture dependent. Further, we propose a regression model that predicts an application's sensitivity and pressure across architectures with high accuracy. This regression model enables a data center scheduler to guarantee the QoS of a VM/application when it is scheduled to co-locate with another VMs/applications.