Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Side information exploitation, quality control and low complexity implementation for distributed video coding

Distributed video coding (DVC) is a new video coding methodology that shifts the highly complex motion search components from the encoder to the decoder, such a video coder would have a great advantage in encoding speed and it is still able to achieve similar rate-distortion performance as the conventional coding solutions. Applications include wireless video sensor networks, mobile video cameras and wireless video surveillance, etc. Although many progresses have been made in DVC over the past ten years, there is still a gap in RD performance between conventional video coding solutions and DVC. The latest development of DVC is still far from standardization and practical use. The key problems remain in the areas such as accurate and efficient side information generation and refinement, quality control between Wyner-Ziv frames and key frames, correlation noise modelling and decoder complexity, etc. Under this context, this thesis proposes solutions to improve the state-of-the-art side information refinement schemes, enable consistent quality control over decoded frames during coding process and implement highly efficient DVC codec. This thesis investigates the impact of reference frames on side information generation and reveals that reference frames have the potential to be better side information than the extensively used interpolated frames. Based on this investigation, we also propose a motion range prediction (MRP) method to exploit reference frames and precisely guide the statistical motion learning process. Extensive simulation results show that choosing reference frames as SI performs competitively, and sometimes even better than interpolated frames. Furthermore, the proposed MRP method is shown to significantly reduce the decoding complexity without degrading any RD performance. To minimize the block artifacts and achieve consistent improvement in both subjective and objective quality of side information, we propose a novel side information synthesis framework working on pixel granularity. We synthesize the SI at pixel level to minimize the block artifacts and adaptively change the correlation noise model according to the new SI. Furthermore, we have fully implemented a state-of-the-art DVC decoder with the proposed framework using serial and parallel processing technologies to identify bottlenecks and areas to further reduce the decoding complexity, which is another major challenge for future practical DVC system deployments. The performance is evaluated based on the latest transform domain DVC codec and compared with different standard codecs. Extensive experimental results show substantial and consistent rate-distortion gains over standard video codecs and significant speedup over serial implementation. In order to bring the state-of-the-art DVC one step closer to practical use, we address the problem of distortion variation introduced by typical rate control algorithms, especially in a variable bit rate environment. Simulation results show that the proposed quality control algorithm is capable to meet user defined target distortion and maintain a rather small variation for sequence with slow motion and performs similar to fixed quantization for fast motion sequence at the cost of some RD performance. Finally, we propose the first implementation of a distributed video encoder on a Texas Instruments TMS320DM6437 digital signal processor. The WZ encoder is efficiently implemented, using rate adaptive low-density-parity-check accumulative (LDPCA) codes, exploiting the hardware features and optimization techniques to improve the overall performance. Implementation results show that the WZ encoder is able to encode at 134M instruction cycles per QCIF frame on a TMS320DM6437 DSP running at 700MHz. This results in encoder speed 29 times faster than non-optimized encoder implementation. We also implemented a highly efficient DVC decoder using both serial and parallel technology based on a PC-HPC (high performance cluster) architecture, where the encoder is running in a general purpose PC and the decoder is running in a multicore HPC. The experimental results show that the parallelized decoder can achieve about 10 times speedup under various bit-rates and GOP sizes compared to the serial implementation and significant RD gains with regards to the state-of-the-art DISCOVER codec

Online Reinforcement Learning for Dynamic Multimedia Systems

In our previous work, we proposed a systematic cross-layer framework for dynamic multimedia systems, which allows each layer to make autonomous and foresighted decisions that maximize the system's long-term performance, while meeting the application's real-time delay constraints. The proposed solution solved the cross-layer optimization offline, under the assumption that the multimedia system's probabilistic dynamics were known a priori. In practice, however, these dynamics are unknown a priori and therefore must be learned online. In this paper, we address this problem by allowing the multimedia system layers to learn, through repeated interactions with each other, to autonomously optimize the system's long-term performance at run-time. We propose two reinforcement learning algorithms for optimizing the system under different design constraints: the first algorithm solves the cross-layer optimization in a centralized manner, and the second solves it in a decentralized manner. We analyze both algorithms in terms of their required computation, memory, and inter-layer communication overheads. After noting that the proposed reinforcement learning algorithms learn too slowly, we introduce a complementary accelerated learning algorithm that exploits partial knowledge about the system's dynamics in order to dramatically improve the system's performance. In our experiments, we demonstrate that decentralized learning can perform as well as centralized learning, while enabling the layers to act autonomously. Additionally, we show that existing application-independent reinforcement learning algorithms, and existing myopic learning algorithms deployed in multimedia systems, perform significantly worse than our proposed application-aware and foresighted learning methods.Comment: 35 pages, 11 figures, 10 table

Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)

Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the sum of absolute differences (SAD). Unfortunately, the SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be approached as an optimization problem, where the goal is to find the best matching block within a search space. The simplest available BM method is the full search algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of SAD values for all elements of the search window. Recently, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the price of poor accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC) optimization is proposed to reduce the number of search locations in the BM process. In our algorithm, the computation of search locations is drastically reduced by considering a fitness calculation strategy which indicates when it is feasible to calculate or only estimate new search locations. Since the proposed algorithm does not consider any fixed search pattern or any other movement assumption as most of other BM approaches do, a high probability for finding the true minimum (accurate motion vector) is expected. Conducted simulations show that the proposed method achieves the best balance over other fast BM algorithms, in terms of both estimation accuracy and computational cost.Comment: 22 Pages. arXiv admin note: substantial text overlap with arXiv:1405.4721, arXiv:1406.448

Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy

In this paper we shall consider the problem of deploying attention to subsets of the video streams for collating the most relevant data and information of interest related to a given task. We formalize this monitoring problem as a foraging problem. We propose a probabilistic framework to model observer's attentive behavior as the behavior of a forager. The forager, moment to moment, focuses its attention on the most informative stream/camera, detects interesting objects or activities, or switches to a more profitable stream. The approach proposed here is suitable to be exploited for multi-stream video summarization. Meanwhile, it can serve as a preliminary step for more sophisticated video surveillance, e.g. activity and behavior analysis. Experimental results achieved on the UCR Videoweb Activities Dataset, a publicly available dataset, are presented to illustrate the utility of the proposed technique.Comment: Accepted to IEEE Transactions on Image Processin

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Machine Learning-Based Quality-Aware Power and Thermal Management of Multistream HEVC Encoding on Multicore Servers

The emergence of video streaming applications, together with the users’ demand for high-resolution contents, has led to the development of new video coding standards, such as High Efficiency Video Coding (HEVC). HEVC provides high efficiency at the cost of increased complexity. This higher computational burden results in increased power consumption in current multicore servers. To tackle this challenge, algorithmic optimizations need to be accompanied by content-aware application-level strategies, able to reduce power while meeting compression and quality requirements. In this paper, we propose a machine learning-based power and thermal management approach that dynamically learns and selects the best encoding configuration and operating frequency for each of the videos running on multicore servers, by using information from frame compression, quality, encoding time, power, and temperature. In addition, we present a resolution-aware video assignment and migration strategy that reduces the peak and average temperature of the chip while maintaining the desirable encoding time. We implemented our approach in an enterprise multicore server and evaluated it under several common scenarios for video providers. On average, compared to a state-of-the-art technique, for the most realistic scenario, our approach improves BD-PSNR and BD-rate by 0.54 dB, and 8%, respectively, and reduces the encoding time, power consumption, and average temperature by 15.3%, 13%, and 10%, respectively. Moreover, our proposed approach increases BD-PSNR and BD-rate compared to the HEVC Test Model (HM), by 1.19 dB and 24%, respectively, without any encoding time degradation, when power and temperature constraints are relaxed