Distributed AI training often suffers from network faults. Network faults, especially at the last hop between a switch and a host, result in loss of connectivity, resulting in training job stalls and eventual failure. This is typically managed through a fail-stop mechanism, followed by a restart, incurring significant inefficiencies. We present ReCCL, the first network fault-tolerant collective communication library (CCL) that allows training progress to be preserved by seamlessly failing over to alternate paths when a network fault occurs. During failover, ReCCL keeps communication states synchronized while using dynamic channel load balancing and intra-host GPU routing to improve communication performance. Our evaluations demonstrate that ReCCL can perform failover seamlessly with minimal performance losses. Additionally, our simulations also demonstrate that failover can be effectively used to achieve significant savings in GPU hours for large-scale distributed AI training workloads.

Accurate and efficient traffic flow prediction is crucial for the increasingly prevalent autonomous driving, enabling more advanced intelligent transportation systems. For this purpose, we propose a novel model termed Meta Guided-Graph Lightweight TimesNet (MGGLTN) to accurately capture the spatio-temporal correlations within traffic flow data, thereby providing precise traffic flow predictions for Connected Vehicles (CVs). Our spatio-temporal information learning architecture features an encoder-decoder backbone, wherein both the encoder and decoder comprise graph convolutional networks coupled with lightweight Times modules. More importantly, we propose a meta guided-graph library, aimed at providing memory queries for time-varying traffic patterns based on real-world physical spatial information. It efficiently guides the initialization direction of meta guided-graph prototypes, thereby accelerating the convergence speed of model training. Moreover, we introduce depthwise separable convolutions to replace the computationally intensive multi-kernel convolutions in the Times modules, thus significantly reducing computational costs and model parameters while maintaining accuracy. We perform extensive experiments on three public benchmark datasets (i.e., METR-LA, PEMS-BAY, and EXPY-TKY) and conduct comprehensive performance evaluations compared to both baseline models and state-of-the-art models. The findings demonstrate the superior performance of our model across all three datasets of varying spatial scales, highlighting the potential of this model to provide precise traffic guidance for CVs.

This paper presents the outcomes of the PETS2025 challenge, held in conjunction with AVSS 2025 and sponsored by the EU-funded EURMARS project. The challenge introduces a novel maritime surveillance dataset comprising image sequences captured by diverse multi-altitude, multimodal sensors, reflecting the real-world multi-authority environment. The key tasks include: (1) object detection using various sensors across different platforms (ground-based and low-altitude aerial) and spectral ranges (visible, thermal, ultraviolet (UV), and short-wave infrared (SWIR)); (2) long-term tracking of targets in maritime environments spanning both sea and land; and (3) approximating target geolocations by using sensor imagery and telemetry data. Performance evaluations of results submitted by 12 international participants are discussed. The results show the effectiveness of these submissions and highlight ongoing challenges posed by heterogeneous sensors and complex environments. These challenges emphasise the need to further improve detection, tracking, and geolocation approximation for maritime and coastal surveillance.

Teacher behavior description is crucial for improving teaching effectiveness and understanding classroom dynamics. Traditional methods rely on meticulous manual observation and recording, which is extremely complex and time-consuming when dealing with many teaching videos. Recently proposed video captioning technologies can automatically analyze and describe object behaviors, reducing manual intervention and providing powerful support for improving teaching effectiveness. Existing video captioning methods, however, fall short in describing teachers’ behaviors because they overlook the temporal progression of teachers’ actions, the background changes between frames, and the frequent interactions between teachers and students in real classroom environments. To address these issues, we propose a video captioning method leveraging a spatio-temporal graph interaction network (STGIN) to describe teachers’ behaviors in classroom scenes. Comprising the teacher–student spatial interaction module (TSI), teacher temporal context module (TTC), and description generator, STGIN captures spatial interaction relationships between teacher and students, extracts temporal dynamics features of the teachers’ behaviors, and precisely generates teacher behavior descriptions. To validate the proposed scheme, we collected a teacher behavioral description (TBD) dataset consisting of 2000 videos. Extensive experiments on our TBD dataset and two generic scene datasets confirm the effectiveness and robustness of the proposed STGIN in real classroom environments.

Nonintrusive load monitoring (NILM) aims to accurately identify appliance-level power consumption patterns solely based on the total household power signal, facilitating fine-grained management of smart grid demands. Previous monitoring methods have often focused on classifying time domain power signals or identifying appliance signatures in the frequency domain, lacking sufficient analysis of diverse, sparsely labeled data across different households in the time-frequency domain. We propose a novel model named frequency-division fusion scattering time-series transformer (FFSTT). Specifically, in addition to NILM task-driven token embedding, self-attention, and feed forward blocks, we innovatively employ dual-tree complex wavelet transform for time-frequency transformation of tokens. Distinct feature extraction methods are applied to low- and high-frequency components, respectively, to efficiently separate and learn the power consumption patterns of different appliances. Furthermore, to achieve effective domain transfer among households in different regions, we utilize a small amount of labeled data to perform low-rank fine-tuning on the pretrained FFSTT. Experiments conducted on the REDD and U.K.-DALE datasets confirm that the proposed model achieves state-of-the-art performance across distinct scenarios of available labeled data.

Existing fully distributed protocols for flocking are built upon the network of mobile agents with only cooperative interactions. Rather than investigating such networks, this paper deals with the problem of leader-follower flocking control over signed communication networks, where there impose less restrictions on the distributions of cooperations and competitions in the network of mobile agents. Firstly, for the second-order dynamics model, a novel state feedback controller that relies only on the relative velocity information of neighboring agents is developed. Secondly, the solvability of flocking control problem is transformed to the asymptotic stability of error system, and the latter is guaranteed by treating the product convergence of infinite super-stochastic matrices. Then, sufficient condition for the solvability of flocking control problem is proposed by establishing the inequality constraints on positive and negative edge weights. Finally, a numerical example is performed to illustrate the correctness of the theoretical result.

Byte-addressable Persistent Storage (BPS), such as persistent memory and CXL-enabled SSDs, has become an extension of main memory. This opens up new possibilities for indexes that operate and persist data directly on the memory bus. Recent learned indexes exploit data distribution and have shown great potential for some workloads. Despite some work proposed for integrating learned indexes into BPS, they are mainly based on Intel’s first-generation persistent memory. The current design suffers from the following problems: 1) Excessive storage line accesses due to large node in learned indexes; 2) Inefficient concurrency control due to volatile cache; 3) Write amplification due to mismatch access granularity.

Fine-tuning pre-trained models with task-specific data can produce customized models effective for downstream tasks. However, operating large-scale such fine-tuning tasks in real time in the data center faces non-trivial challenges, including unpredictable task arrival and system environment dynamics, complex deadline-driven fine-tuning scheduling, and intertwined task pricing and cost management. In this paper, targeting the popular Low-Rank Adaptation (LoRA) fine-tuning technique, we present the design and study of a novel auction-based mechanism to jointly schedule and price LoRA tasks in an online manner. We first model the social welfare maximization problem as an integer program for the fine-tuning service provider, capturing all the aforementioned challenges. Then, to solve this NP-hard problem online, we equivalently reformulate this original problem into a schedule selection problem, where each schedule corresponds to a concrete pre-specified operation plan over time for a task. We can thus design a polynomial-time online approximation algorithm via the online primal-dual method to determine the schedule, and with the dual variables, also determine the pricing for each admitted task. We rigorously prove the competitiveness of our online approach against the offline optimum, and prove the economic properties of truthfulness and individual rationality regarding pricing. Finally, we conduct extensive experiments and have validated the substantial advantages of our approach compared to existing methods.

With the rapid development and wide deployment of high-speed network technologies like RDMA and the relatively stagnant evolution of intra-host resources, intra-host network congestion has become a potential issue that may affect the QoS of network applications. Offloading hotspot data to modern Smart-NICs, enabling hotspot data access completion on the SmartNIC, and reducing intra-host network traffic, is a promising solution to this issue. However, due to the limited SmartNIC resources and the complexity of network application requirements, achieving efficient offload is challenging.We present Magician, an architecture to mitigate intra-host network congestion with SmartNIC. Magician adopts a client-driven data access approach to avoid performance degradation caused by limited SmartNIC resources. Magician also introduces a SmartNIC-oriented hotspot data update strategy that dynamically refreshes hotspot data with minimal overhead. Moreover, we design a server-centric data consistency mechanism to ensure data consistency under concurrent access. We implement Magician within the key-value store. Evaluation of the key-value store with and without Magician suggests that, in the presence of intra-host network congestion, Magician significantly mitigates intra-host network congestion, leading to improved performance of network applications.

MOTIVATION: Complex tissues are dynamic ecosystems consisting of molecularly distinct yet interacting cell types. Computational deconvolution aims to dissect bulk tissue data into cell type compositions and cell-specific expressions. With few exceptions, most existing deconvolution tools exploit supervised approaches requiring various types of references that may be unreliable or even unavailable for specific tissue microenvironments. RESULTS: We previously developed a fully unsupervised deconvolution method-Convex Analysis of Mixtures (CAM), that enables estimation of cell type composition and expression from bulk tissues. We now introduce CAM3.0 tool that improves this framework with three new and highly efficient algorithms, namely, radius-fixed clustering to identify reliable markers, linear programming to detect an initial scatter simplex, and a smart floating search for the optimum latent variable model. The comparative experimental results obtained from both realistic simulations and case studies show that the CAM3.0 tool can help biologists more accurately identify known or novel cell markers, determine cell proportions, and estimate cell-specific expressions, complementing the existing tools particularly when study- or datatype-specific references are unreliable or unavailable. AVAILABILITY AND IMPLEMENTATION: The open-source R Scripts of CAM3.0 is freely available at https://github.com/ChiungTingWu/CAM3/(https://github.com/Bioconductor/Contributions/issues/3205). A user's guide and a vignette are provided.

Local associations refer to spatial-temporal correlations that emerge from the biological realm, such as time-dependent gene co-expression or seasonal interactions between microbes. One can reveal the intricate dynamics and inherent interactions of biological systems by examining the biological time series data for these associations. To accomplish this goal, local similarity analysis algorithms and statistical methods that facilitate the local alignment of time series and assess the significance of the resulting alignments have been developed. Although these algorithms were initially devised for gene expression analysis from microarrays, they have been adapted and accelerated for multi-omics next generation sequencing datasets, achieving high scientific impact. In this review, we present an overview of the historical developments and recent advances for local similarity analysis algorithms, their statistical properties, and real applications in analyzing biological time series data. The benchmark data and analysis scripts used in this review are freely available at http://github.com/labxscut/lsareview.

As a new distributed machine learning (ML) framework for privacy protection, federated learning (FL) enables substantial Internet of Things (IoT) devices (e.g., mobile phones, tablets, etc.) to participate in collaborative training of an ML model. FL can protect the data privacy of IoT devices without exposing their raw data. However, the diversity of IoT devices may degrade the overall training process due to the straggler issue. To tackle this problem, we propose a gear-based asynchronous FL (AsyFed) architecture. It adds a gear layer between the clients and the FL server as a mediator to store the model parameters. The key insight is that we group these clients with similar training abilities into the same gear. The clients within the same gear conduct synchronous training. These gears then communicate with the global FL server asynchronously. Besides, we propose a T-step mechanism to reduce the weight from the slow gear when they are communicating with the FL server. The extensive experiment evaluations indicate that AsyFed outperforms FedAvg (baseline synchronous FL scheme) and some state-of-the-art asynchronous FL methods in terms of training accuracy or speed under different data distributions. The only negligible overhead is that we leverage the extra layer (gear layer) to preserve part of the model parameters.

Edge computing can provide high bandwidth and low-latency service for big data tasks by leveraging the edge side’s computing, storage, and network resources. With the development of microservice and docker technology, service providers can flexibly and dynamically cache microservice at the edge side to respond efficiently with limited resources. Automatically caching needed services on the nearest edge nodes and dynamically scheduling users’ requests can realize that computing power and software services flow with the users to provide continuous services. However, achieving the goal needs to overcome many challenges, such as the significant fluctuation of user devices’ requests at the edge side and the lack of collaboration among edge nodes. In this article, dynamic computing power scheduling and collaborative task scheduling among edge nodes are comprehensively developed. The problem is considered a multiobjective optimization problem, including sequentially minimizing the deadline missing rate of requests and the average task completion time. We propose an adaptive mechanism for dynamically collaborative computing power and task scheduling (ADCS) in the edge environment to solve this problem. It adopts the greedy decision method to schedule computing tasks to meet their deadline requirements. At the same time, it uses the best-fit method to adjust the computing resources according to the changes of users’ requests. The simulation results show that ADCS can decrease the deadline missing rate and reduce the average completion time. Compared with DSR and CoDSR, the deadline missing rate is reduced by 59.91% and 19.95%, respectively. The average completion time is decreased by 37.87% and 6.71%.

MOTIVATION: Complex biological tissues are often a heterogeneous mixture of several molecularly distinct cell subtypes. Both subtype compositions and subtype-specific (STS) expressions can vary across biological conditions. Computational deconvolution aims to dissect patterns of bulk tissue data into subtype compositions and STS expressions. Existing deconvolution methods can only estimate averaged STS expressions in a population, while many downstream analyses such as inferring co-expression networks in particular subtypes require subtype expression estimates in individual samples. However, individual-level deconvolution is a mathematically underdetermined problem because there are more variables than observations. RESULTS: We report a sample-wise Convex Analysis of Mixtures (swCAM) method that can estimate subtype proportions and STS expressions in individual samples from bulk tissue transcriptomes. We extend our previous CAM framework to include a new term accounting for between-sample variations and formulate swCAM as a nuclear-norm and ℓ2,1-norm regularized matrix factorization problem. We determine hyperparameter values using cross-validation with random entry exclusion and obtain a swCAM solution using an efficient alternating direction method of multipliers. Experimental results on realistic simulation data show that swCAM can accurately estimate STS expressions in individual samples and successfully extract co-expression networks in particular subtypes that are otherwise unobtainable using bulk data. In two real-world applications, swCAM analysis of bulk RNASeq data from brain tissue of cases and controls with bipolar disorder or Alzheimer's disease identified significant changes in cell proportion, expression pattern and co-expression module in patient neurons. Comparative evaluation of swCAM versus peer methods is also provided. AVAILABILITY AND IMPLEMENTATION: The R Scripts of swCAM are freely available at https://github.com/Lululuella/swCAM. A user's guide and a vignette are provided. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The wide deployment of advanced robots with industrial IoT (IIoT) technologies in smart factories generates a large volume of data during production and a wide variety of data processing workloads are launched to maintain productivity and safety of smart manufacture. The emerging fog computing paradigm offers a promising solution to enhancing data processing performance in a smart factory environment while on the other hand brings in new challenges to resource management, which call for a more effective approach for recommending resource configurations to heterogeneous workloads. In this paper, we propose an Optimized Recommendations of Heterogeneous Resource Configurations (ORHRC) model that employs machine learning techniques to provide resource configuration recommendations for the heterogeneous workloads in a fog computing-based smart factory environment. ORHRC learns a recommendation model by leveraging the operating characteristics and execution time of workloads on fog servers with different configurations. We also design a decision model in ORHRC to further improve prediction accuracy and reduce operational overheads. Experiment results show that ORHRC outperforms the state of art configuration recommendation methods in terms of average prediction accuracy.Note to Practitioners—The various data processing workloads in a smart factory environment need to be processed by the computational resources with optimal configurations for meeting their performance requirements. In this paper, we employ machine learning technologies for enabling automatic recommendation of resource configurations to heterogeneous workloads. Specifically, we develop an Optimized Recommendations of Heterogeneous Resource Configurations (ORHRC) model that can identify the optimal resource configurations for various workloads. We also conducted extensive experiments that verify the effectiveness of the proposed ORHRC model.

This work presents a novel multimodal biometric dataset with emerging biometric traits including 3D face, thermal face, iris on-the-move, iris mobile, somatotype and smartphone sensors. This dataset was created to resemble on-the-move characteristics in applications such as border control. The five types of biometric traits were selected as they can be captured while on-the-move, are contactless, and show potential for use in a multimodal fusion verification system in a border control scenario. Innovative sensor hardware was used in the data capture. The data featuring these biometric traits will be a valuable contribution to advancing biometric fusion research in general. Baseline evaluation was performed on each unimodal dataset. Multimodal fusion was evaluated based on various scenarios for comparison. Real-time performance is presented based on an Automated Border Control (ABC) scenario.

The edge-cloud hybrid environment requires complex deployment strategies to enable the smart Internet-of-Things (IoT) system. However, current service deployment strategies use simple, generalized heuristics and ignore the heterogeneous characteristics in the edge-cloud hybrid environment. In this article, we devise a method to find a microservice-based service deployment strategy that can reduce the average waiting time of IoT devices in the hybrid environment. For this purpose, we first propose a microservice-based deployment problem (MSDP) based on the heterogeneous and dynamic characteristics in the edge-cloud hybrid environment, including heterogeneity of edge server capacities, dynamic geographical information of IoT devices, and changing device preference for applications and complex application structures. We then propose a multiple buffer deep deterministic policy gradient (MB_DDPG) to provide more preferable service deployment solutions. Our algorithm leverages reinforcement learning and neural network to learn a deployment strategy without any human instruction. Therefore, the service provider can make full use of limited resources to improve the Quality of Service (QoS). Finally, we implement MB_DDPG based on real-world data sets and some synthetic data, and we also implement another two algorithms, genetic algorithm and random algorithm, as a contrast. The experimental results demonstrate that MB_DDPG is able to learn a preferable strategy which, in terms of average waiting time, outperforms genetic algorithm and the random algorithm by 32% and 44%, respectively.

This paper formulates and solves an asynchronous sampled-data containment control problem of second-order multi-agent systems, in which each agent only receives the neighbors' information at certain sampling instants determined by its own clock, not all sampling instants. It is not assumed that the time sequence in which each agent receives its neighbors' information is evenly spaced. A distributed containment control protocol in the asynchronous sampled-data setting is designed. Main research tools, including nonnegative matrix theory and the composite of binary relation, are used to derive a necessary and sufficient condition guaranteeing that all the followers asymptotically converge to a convex hull formed by the static leaders. An example is provided to demonstrate the effectiveness of our theoretical result.

SUMMARY: We develop a fully unsupervised deconvolution method to dissect complex tissues into molecularly distinctive tissue or cell subtypes based on bulk expression profiles. We implement an R package, deconvolution by Convex Analysis of Mixtures (debCAM) that can automatically detect tissue/cell-specific markers, determine the number of constituent subtypes, calculate subtype proportions in individual samples and estimate tissue/cell-specific expression profiles. We demonstrate the performance and biomedical utility of debCAM on gene expression, methylation, proteomics and imaging data. With enhanced data preprocessing and prior knowledge incorporation, debCAM software tool will allow biologists to perform a more comprehensive and unbiased characterization of tissue remodeling in many biomedical contexts. AVAILABILITY AND IMPLEMENTATION: http://bioconductor.org/packages/debCAM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

While non-negative blind source separation (nBSS) has found many successful applications in science and engineering, model order selection, determining the number of sources, remains a critical yet unresolved problem. Various model order selection methods have been proposed and applied to real-world data sets but with limited success, with both order over- and under-estimation reported. By studying existing schemes, we have found that the unsatisfactory results are mainly due to invalid assumptions, model oversimplification, subjective thresholding, and/or to assumptions made solely for mathematical convenience. Building on our earlier work that reformulated model order selection for nBSS with more realistic assumptions and models, we report a newly and formally revised model order selection criterion rooted in the minimum description length (MDL) principle. Adopting widely invoked assumptions for achieving a unique nBSS solution, we consider the mixing matrix as consisting of deterministic unknowns, with the source signals following a multivariate Dirichlet distribution. We derive a computationally efficient, stochastic algorithm to obtain approximate maximum-likelihood estimates of model parameters and apply Monte Carlo integration to determine the description length. Our modeling and estimation strategy exploits the characteristic geometry of the data simplex in nBSS. We validate our nBSS-MDL criterion through extensive simulation studies and on four real-world data sets, demonstrating its strong performance and general applicability to nBSS. The proposed nBSS-MDL criterion consistently detects the true number of sources, in all of our case studies.

This work presents the 2ndCross-Spectrum Iris/Periocular Recognition Competition (Cross-Eyed2017). The main goal of the competition is to promote and evaluate advances in cross-spectrum iris and periocular recognition. This second edition registered an increase in the participation numbers ranging from academia to industry: five teams submitted twelve methods for the periocular task and five for the iris task. The benchmark dataset is an enlarged version of the dual-spectrum database containing both iris and periocular images synchronously captured from a distance and within a realistic indoor environment. The evaluation was performed on an undisclosed test-set. Methodology, tested algorithms, and obtained results are reported in this paper identifying the remaining challenges in path forward.

Anti-spoofing is attracting growing interest in biometrics , considering the variety of fake materials and new means to attack biometric recognition systems. New unseen materials continuously challenge state-of-the-art spoofing detectors, suggesting for additional systematic approaches to target anti-spoofing. By incorporating liveness scores into the biometric fusion process, recognition accuracy can be enhanced, but traditional sum-rule based fusion algorithms are known to be highly sensitive to single spoofed instances. This paper investigates 1-median filtering as a spoofing-resistant generalised alternative to the sum-rule targeting the problem of partial multibiometric spoofing where m out of n biometric sources to be combined are attacked. Augmenting previous work, this paper investigates the dynamic detection and rejection of liveness-recognition pair outliers for spoofed samples in true multi-modal configuration with its inherent challenge of normalisation. As a further contribution, bootstrap aggregating (bagging) classifiers for fingerprint spoof-detection algorithm is presented. Experiments on the latest face video databases (Idiap Replay-Attack Database and CASIA Face Anti-Spoofing Database) and fingerprint spoofing database (Fingerprint Liveness Detection Competition 2013) illustrate the efficiency of proposed techniques.

SUMMARY: We develop a novel unsupervised deconvolution method, within a well-grounded mathematical framework, to dissect mixed gene expressions in heterogeneous tumor samples. We implement an R package, UNsupervised DecOnvolution (UNDO), that can be used to automatically detect cell-specific marker genes (MGs) located on the scatter radii of mixed gene expressions, estimate cellular proportions in each sample and deconvolute mixed expressions into cell-specific expression profiles. We demonstrate the performance of UNDO over a wide range of tumor-stroma mixing proportions, validate UNDO on various biologically mixed benchmark gene expression datasets and further estimate tumor purity in TCGA/CPTAC datasets. The highly accurate deconvolution results obtained suggest not only the existence of cell-specific MGs but also UNDO's ability to detect them blindly and correctly. Although the principal application here involves microarray gene expressions, our methodology can be readily applied to other types of quantitative molecular profiling data. AVAILABILITY AND IMPLEMENTATION: UNDO is available at http://bioconductor.org/packages.

Multibiometrics aims at improving biometric security in presence of spoofing attempts, but exposes a larger availability of points of attack. Standard fusion rules have been shown to be highly sensitive to spoofing attempts - even in case of a single fake instance only. This paper presents a novel spoofing-resistant fusion scheme proposing the detection and elimination of anomalous fusion input in an ensemble of evidence with liveness information. This approach aims at making multibiometric systems more resistant to presentation attacks by modeling the typical behaviour of human surveillance operators detecting anomalies as employed in many decision support systems. It is shown to improve security, while retaining the high accuracy level of standard fusion approaches on the latest Fingerprint Liveness Detection Competition (LivDet) 2013 dataset.

This paper presents a multiview-video-plus-depth coding scheme, which is compatible with the advanced video coding (H.264/AVC) standard and its multiview video coding (MVC) extension. This scheme introduces several encoding and in-loop coding tools for depth and texture video coding, such as depth-based texture motion vector prediction, depth-range-based weighted prediction, joint inter-view depth filtering, and gradual view refresh. The presented coding scheme is submitted to the 3D video coding (3DV) call for proposals (CfP) of the Moving Picture Experts Group standardization committee. When measured with commonly used objective metrics against the MVC anchor, the proposed scheme provides an average bitrate reduction of 26% and 35% for the 3DV CfP test scenarios with two and three views, respectively. The observed bitrate reduction is similar according to an analysis of the results obtained for the subjective tests on the 3DV CfP submissions.

Depth-enhanced multiview video, such as the multiview video plus depth (MVD) format, can be used to provide displaying-time view adjustment capability through depth-image-based rendering (DIBR) and additional compression improvement compared to the Multiview Video Coding standard. In this paper a gradual view refresh (GVR) method is presented to code random access points and provide fast startup in streaming for MVD bitstreams. When decoding is started from a GVR point, a subset of the views can be accurately decoded, while the remaining views can be approximately reconstructed using DIBR. Perfect reconstruction of all views can be reached at a subsequent random access point. The GVR coding was found to be effective with up to 10% bitrate reduction for sequences with static camera arrangement. The use of GVR for fast startup in video streaming was found to be clearly superior to transmitting a MVD bitstream conventionally from rate-distortion point of view. It has been found in earlier studies that there seems to be a delay from stimulus onset until depth is fully perceived, hence giving a reason to believe that accurate reconstruction of all views might not be necessary immediately after starting decoding. Furthermore, it was observed in this paper that the objective picture quality reduction during GVR was only moderate, verifying the applicability of the presented GVR method.

Depth maps, an essential part in the new generation of 3D video coding, allow rendering of arbitrary viewpoints of a video scene. Depth maps are characterized by sharp object boundaries, which significantly affect the rendering quality and account for the most bitrate for depth map coding. This paper proposes a novel intra coding method for depth maps based on a two-step adaptive boundary location process. By extracting a series of sub-blocks along a depth boundary and refining the boundary within sub-blocks, accurate predictions for blocks with arbitrary edge shapes can be realized. Experimental results show that the proposed scheme achieves bitrate reductions of up to 28% and 13% on average for seven test sequences of MPEG 3DV compared to original intra coding of H.264/AVC considering the same quality of synthesized views. Besides, subjective quality of virtual views is improved owning to well preserved boundary information.