Hongkai Xiong

Transmission Distortion Analysis for Real-Time Video Encoding and Streaming Over Wireless Networks

A major challenge in video encoding and transmission over wireless networks is that the channel is error-prone while the compressed video data is highly sensitive to errors. The transmission errors will cause decoding failure at the receiver side. More importantly, the transmission errors introduced in one video frame will propagate to its subsequent frames along the motion prediction path and significantly degrade video presentation quality. This type of picture distortion is called transmission distortion. In this work, we propose a control system approach to transmission distortion modeling. More specifically, we consider the wireless video transmission and decoding system as a linear system with the transmission errors as system input and the transmission distortion as output. We study the fading behavior of the impulse transmission distortion. We analyze the low-pass filtering behavior of video encoders and develop a scheme to estimate the instantaneous transmission distortion. Our extensive simulation results demonstrate that the proposed transmission distortion model is accurate and robust. In addition, it has very low computational complexity. More importantly, because it is a predictive model, it allows the encoder to predict the transmission distortion even before the video is compressed and transmitted. This type of transmission distortion model has important applications in resource allocation and performance optimization in real-time wireless video communication

Optimal Power Allocation for Hybrid Overlay/Underlay Spectrum Sharing in Multiband Cognitive Radio Networks

In this paper, we consider power allocation in multiband cognitive radio (CR) networks, where multiple secondary users (SUs) transmit via a common relay and compete for the transmit power of the relay. We employ a hybrid overlay/underlay spectrum sharing scheme, allowing the SU to adapt its way of accessing the licensed spectrum to the status of the primary user (PU). If the PU is detected to be idle at the selected channel, the SU works in an overlay mode; else, it works in spectrum underlay. In addition, an auction-based power-allocation scheme is proposed to solve power competition of multiple SUs. For the SU working in spectrum overlay, the relay allocates the power in proportion to its payment without additional constraints; for the SU in spectrum underlay, its own transmit power and that of the relay are upper bounded for the quality of service (QoS) of the PU. Then, the convergence of the proposed auction algorithm and the outage probability of secondary transmissions is theoretically analyzed. Finally, the performance of the proposed scheme is verified by the simulation results.

Picking Deep Filter Responses for Fine-Grained Image Recognition

Recognizing fine-grained sub-categories such as birds and dogs is extremely challenging due to the highly localized and subtle differences in some specific parts. Most previous works rely on object / part level annotations to build part-based representation, which is demanding in practical applications. This paper proposes an automatic fine-grained recognition approach which is free of any object / part annotation at both training and testing stages. Our method explores a unified framework based on two steps of deep filter response picking. The first picking step is to find distinctive filters which respond to specific patterns significantly and consistently, and learn a set of part detectors via iteratively alternating between new positive sample mining and part model retraining. The second picking step is to pool deep filter responses via spatially weighted combination of Fisher Vectors. We conditionally pick deep filter responses to encode them into the final representation, which considers the importance of filter responses themselves. Integrating all these techniques produces a much more powerful framework, and experiments conducted on CUB-200-2011 and Stanford Dogs demonstrate the superiority of our proposed algorithm over the existing methods.

Joint Coding/Routing Optimization for Distributed Video Sources in Wireless Visual Sensor Networks

This paper studies a joint coding/routing optimization between network lifetime and video distortion by applying information theory to wireless visual sensor networks for correlated sources. Arbitrary coding [distributed video coding and network coding (NC)] from both combinatorial optimization and information theory could make significant progress toward the performance limit and tractable. Also, multipath routing can spread energy utilization across nodes within the entire network to keep a potentially longer lifetime, and solve the wireless contention issues by the splitting traffic. The objective function not only keeps the total energy consumption of encoding power, transmission power, and reception power minimized, but ensures the information received by sink nodes to approximately reconstruct the visual field. Also, a generalized power consumption model for distributed video sources is developed, in which the coding complexity of Key frames and Wyner-Ziv frames is measured by translating specific coding behavior into energy consumption. On the basis of the distributed multiview video coding and NC-based multipath routing, the balance problem between lifetime (costs) and distortion (capacity) is modeled as an optimization formulation with a fully distributed solution. Through a primal decomposition, a two-level optimization is relaxed with Lagrangian dualization and solved by the gradient algorithm. The low-level optimization problem is further decomposed into a secondary master dual problem with four cross-layer subproblems: a rate control problem, a channel contention problem, a distortion control problem, and an energy conservation problem. The implementation of the distributed algorithm is discussed with regard to the communication overhead and dynamic network change. Simulation results validate the convergence and performance of the proposed algorithm.

HEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS

MOTIVATION Genome-wide association studies (GWAS) have been widely used in discovering the association between genotypes and phenotypes. Human genome data contain valuable but highly sensitive information. Unprotected disclosure of such information might put individuals privacy at risk. It is important to protect human genome data. Exact logistic regression is a bias-reduction method based on a penalized likelihood to discover rare variants that are associated with disease susceptibility. We propose the HEALER framework to facilitate secure rare variants analysis with a small sample size. RESULTS We target at the algorithm design aiming at reducing the computational and storage costs to learn a homomorphic exact logistic regression model (i.e. evaluate P-values of coefficients), where the circuit depth is proportional to the logarithmic scale of data size. We evaluate the algorithm performance using rare Kawasaki Disease datasets. AVAILABILITY AND IMPLEMENTATION Download HEALER at http://research.ucsd-dbmi.org/HEALER/ CONTACT: shw070@ucsd.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Lifetime and Distortion Optimization With Joint Source/Channel Rate Adaptation and Network Coding-Based Error Control in Wireless Video Sensor Networks

In this paper, we study joint performance optimization on network lifetime and video distortion for an energy-constrained wireless video sensor network (WVSN). To seek an appropriate tradeoff between maximum network lifetime and minimum video distortion, a framework for joint source/channel rate adaptation is proposed, where the video encoding rate, link rate, and power consumption are jointly considered, formulating a weighted convex optimization problem. In the context of lossy wireless channels, an efficient error control scheme that couples network coding and multipath routing is explored. Moreover, an integrated power consumption model, including power dissipation on video compression, error control, and data communication, is specifically developed for the video sensor node. By primal decomposition, the original problem is decomposed into a two-level optimization procedure, with the high-level procedure for source adaptation (source rate optimization) and the low-level procedure for channel adaptation (network resource allocation). Finally, a fully decentralized iterative algorithm is developed to resolve the target optimization problem. Extensive simulation results evaluate the convergence performance of the proposed algorithm and demonstrate the best tradeoff performance.

Rate control for real-time video network transmission on end-to-end rate-distortion and application-oriented QoS

In the merged multimedia packet-switched networks, the limited or no end-to-end Quality of Service (QoS) guarantees induce that rate control has been evolved to joint source-channel adjustment architecture based on application-oriented QoS. Based on the statistical analysis under the spatial intra and temporal inter prediction subject to a universal spatial-temporal coding framework and a general error concealment by the decoder, an end-to-end distortion estimation model is proposed. On the basis of the analytic model, this paper fulfills a picture quality parameters selection solution with rate-distortion (R-D) Lagrange optimization for a general coding engine including H.264/AVC, exploiting either the source-driven iterative prediction or feedback recursion. Further, a corresponding joint source-channel rate control strategy is proposed. For the real-time variable bit-rate (VBR) video transmission under a given time-varying network condition, the strategy could estimate an instantaneous available transmission rate on traffic smoothing and codecs buffer control, adopt the proposed an end-to-end distortion regressive model and a global optimal error control parameters selection, and address the consistent bit allocation in group of picture (GOP) level, picture level, and MB level. The extensive network simulation experiments show a better and more consistent end-to-end picture quality, in contrast with the locally optimal control strategy at MB level.

A joint rate control scheme for H.264 encoding of multiple video sequences

The objective of joint rate control is to dynamically distribute the channel capacity among video sequences according to their respective complexities, thus a more uniform picture quality and a more efficient utilization of channel capacity are achieved. Most existing approaches are based on MPEG2 coding platform. This paper presents a novel joint rate control scheme for multiple video sequences coding based on H.264 standard. The joint rate control is performed hierarchically from a group of pictures of all sequences down to each picture of every sequence, depending on both the complexity of frames and the buffer occupancy. A novel complexity measure that adapts to the characteristics of H.264 video coding is proposed. An improved joint buffer control strategy, which is extended from that in the single sequence rate control scheme, is applied. Experimental results show that the proposed scheme maintains a good balance in picture quality among the sequences as well as within a sequence.

Image compression with structure-aware inpainting

This paper carves out a way to image compression that is motivated by the recent advancement in image inpainting. An image coding approach is proposed in which a number of regions of the input image are skipped at the encoder and are recovered through the inpainting process at the decoder. Furthermore, a structure-aware inpainting (SAI) method is developed to restore the skipped structural regions by taking advantage of the available portion of the decoded image. A binary structure map is extracted and compressed into the generated bit-stream to indicate the skipped regions with salient structures. By making use of the decoded texture information together with the structure map, the SAI method can recover the skipped structural regions as well as the non-structural ones effectively at the decoder. Compared with JPEG, our proposed image compression scheme allows smaller file, with the potential of up to 50% bit-saving capability, at similar visual quality levels

FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption

BackgroundThe increasing availability of genome data motivates massive research studies in personalized treatment and precision medicine. Public cloud services provide a flexible way to mitigate the storage and computation burden in conducting genome-wide association studies (GWAS). However, data privacy has been widely concerned when sharing the sensitive information in a cloud environment.MethodsWe presented a novel framework (FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption) to fully outsource GWAS (i.e., chi-square statistic computation) using homomorphic encryption. The proposed framework enables secure divisions over encrypted data. We introduced two division protocols (i.e., secure errorless division and secure approximation division) with a trade-off between complexity and accuracy in computing chi-square statistics.ResultsThe proposed framework was evaluated for the task of chi-square statistic computation with two case-control datasets from the 2015 iDASH genome privacy protection challenge. Experimental results show that the performance of FORESEE can be significantly improved through algorithmic optimization and parallel computation. Remarkably, the secure approximation division provides significant performance gain, but without missing any significance SNPs in the chi-square association test using the aforementioned datasets.ConclusionsUnlike many existing HME based studies, in which final results need to be computed by the data owner due to the lack of the secure division operation, the proposed FORESEE framework support complete outsourcing to the cloud and output the final encrypted chi-square statistics.