Yiannis Andreopoulos

University College London, Londinium, England, United Kingdom

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Publications (92)134.12 Total impact

  • Fabio Verdicchio, Yiannis Andreopoulos
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    ABSTRACT: Multimedia analysis, enhancement and coding methods often resort to adaptive transforms that exploit local characteristics of the input source. Following the signal decomposition stage, the produced transform coefficients and the adaptive transform parameters can be subject to quantization and/or data corruption (e.g. due to transmission or storage limitations). As a result, mismatches between the analysis- and synthesis-side transform coefficients and adaptive parameters may occur, severely impacting the reconstructed signal and therefore affecting the quality of the subsequent analysis, processing and display task. Hence, a thorough understanding of the quality degradation ensuing from such mismatches is essential for multimedia applications that rely on adaptive signal decompositions. This paper focuses on lifting-based adaptive transforms that represent a broad class of adaptive decompositions. By viewing the mismatches in the transform coefficients and the adaptive parameters as perturbations in the synthesis system, we derive analytic expressions for the expected reconstruction distortion. Our theoretical results are experimentally assessed using 1D adaptive decompositions and motion-adaptive temporal decompositions of video signals.
    Image and Vision Computing 10/2011; 29(11):744-758. DOI:10.1016/j.imavis.2011.08.004 · 1.58 Impact Factor
  • D. Anastasia, Y. Andreopoulos
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    ABSTRACT: The generic matrix multiply (GEMM) subprogram is the core element of high-performance linear algebra software used in computationally-demanding digital signal processing (DSP) systems. We propose an acceleration technique for GEMM based on dynamically adjusting the precision of computation. Our technique employs DSP methods (such as scalar companding and rounding), followed by a new form of tight packing in floating-point that allows for concurrent calculation of multiple results. Since the companding process controls the increase of concurrency (via packing), the increase in processing throughput (and the corresponding loss in precision) depends on the input data statistics: low-variance parts of the matrix multiplication are computed faster than high-variance parts and the error is controlled in a stochastic and not in a worst-case sense. This can convert high-performance numerical DSP libraries into a computation channel where the output error increases when higher throughput is requested. Potential DSP applications that can benefit from the proposed approach are highlighted.
    Digital Signal Processing (DSP), 2011 17th International Conference on; 08/2011
  • Fabio Verdicchio, Yiannis Andreopoulos
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    ABSTRACT: In video communication systems, due to quantization and transmission errors, mismatches between the transmitter - and receiver-side information may occur, severely impacting the reconstructed video. Theoretical understanding of the quality degradation ensuing from such mismatches is essential when targeting quality-of-service for video communications. In this paper, by viewing the mismatches in the transform coefficients and the adaptive parameters of the temporal analysis of a video coding system as perturbations in the synthesis system, we derive analytic approximations for the expected reconstruction distortion. Our theoretical results are experimentally assessed using adaptive temporal decompositions within a video coding system based on motion-adaptive temporal lifting decomposition. Since we focus on the generic case of adaptive lifting transforms, our results can provide useful insights for estimation-theoretic resiliency mechanisms to be considered within standardized transform-based codecs.
    18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, Belgium, September 11-14, 2011; 01/2011
  • Yiannis Andreopoulos, Dai Jiang, Andreas Demosthenous
    [Show abstract] [Hide abstract]
    ABSTRACT: It was proposed recently that quantized representations of the input source (e.g., images, video) can be used for the computation of the two-dimensional discrete wavelet transform (2D DWT) incrementally. The coarsely quantized input source is used for the initial computation of the forward or inverse DWT, and the result is successively refined with each new refinement of the source description via an embedded quantizer. This computation is based on the direct two-dimensional factorization of the DWT using the generalized spatial combinative lifting algorithm. In this correspondence, we investigate the use of prediction for the computation of the results, i.e., exploiting the correlation of neighboring input samples (or transform coefficients) in order to reduce the dynamic range of the required computations, and thereby reduce the circuit activity required for the arithmetic operations of the forward or inverse transform. We focus on binomial factorizations of DWTs that include (amongst others) the popular 9/7 filter pair. Based on an FPGA arithmetic co-processor testbed, we present energy-consumption results for the arithmetic operations of incremental refinement and prediction-based incremental refinement in comparison to the conventional (nonrefinable) computation. Our tests with combinations of intra and error frames of video sequences show that the former can be 70% more energy efficient than the latter for computing to half precision and remains 15% more efficient for full-precision computation.
    IEEE Transactions on Signal Processing 09/2010; DOI:10.1109/TSP.2010.2048707 · 3.20 Impact Factor
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    D. Anastasia, Y. Andreopoulos
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    ABSTRACT: Computer hardware with native support for large-bitwidth operations can be used for the concurrent calculation of multiple independent linear image processing operations when these operations map integers to integers. This is achieved by packing multiple input samples in one large-bitwidth number, performing a single operation with that number and unpacking the results. We propose an operational framework for tight packing, i.e., achieve the maximum packing possible by a certain implementation. We validate our framework on floating-point units natively supported in mainstream programmable processors. For image processing tasks where operational tight packing leads to increased packing in comparison to previously-known operational packing, the processing throughput is increased by up to 25%.
    IEEE Signal Processing Letters 05/2010; DOI:10.1109/LSP.2010.2041583 · 1.64 Impact Factor
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    Davide Anastasia, Yiannis Andreopoulos
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    ABSTRACT: Software realizations of computationally-demanding image processing tasks (e.g., image transforms and convolution) do not currently provide graceful degradation when their clock-cycles budgets are reduced, e.g., when delay deadlines are imposed in a multitasking environment to meet throughput requirements. This is an important obstacle in the quest for full utilization of modern programmable platforms' capabilities since worst-case considerations must be in place for reasonable quality of results. In this paper, we propose (and make available online) platform-independent software designs performing bitplane-based computation combined with an incremental packing framework in order to realize block transforms, 2-D convolution and frame-by-frame block matching. The proposed framework realizes incremental computation: progressive processing of input-source increments improves the output quality monotonically. Comparisons with the equivalent nonincremental software realization of each algorithm reveal that, for the same precision of the result, the proposed approach can lead to comparable or faster execution, while it can be arbitrarily terminated and provide the result up to the computed precision. Application examples with region-of-interest based incremental computation, task scheduling per frame, and energy-distortion scalability verify that our proposal provides significant performance scalability with graceful degradation.
    IEEE Transactions on Image Processing 03/2010; 19(8):2099-114. DOI:10.1109/TIP.2010.2045702 · 3.11 Impact Factor
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    Davide Anastasia, Yiannis Andreopoulos
    [Show abstract] [Hide abstract]
    ABSTRACT: Ubiquitous image processing tasks (such as transform decompositions, filtering and motion estimation) do not currently provide graceful degradation when their clock-cycles budgets are reduced, e.g. when delay deadlines are imposed in a multi-tasking environment to meet throughput requirements. This is an important obstacle in the quest for full utilization of modern programmable platforms' capabilities, since: (i) worst-case considerations must be in place for reasonable quality of results; (ii) throughput-distortion tradeoffs are not possible for distortion-tolerant image processing applications without cumbersome (and potentially costly) system customization. In this paper, we extend the functionality of the recently-proposed software framework for operational refinement of image processing (ORIP) and demonstrate its inherent throughput-distortion and energy-distortion scalability. Importantly, our extensions allow for such scalabilities at the software level, without needing hardware-specific customization. Extensive tests on a mainstream notebook computer and on OLPC's subnotebook ("xo-laptop") verify that the proposed designs provide for: (i) seamless quality-complexity scalability per video frame; (ii) up to 60% increase in processing throughput with graceful degradation in output quality; (iii) up to 20% more images captured and filtered for the same power-level reduction on the xo-laptop.
    Design, Automation and Test in Europe, DATE 2010, Dresden, Germany, March 8-12, 2010; 01/2010
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    D. Anastasia, Y. Andreopoulos
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    ABSTRACT: We propose software designs that perform incremental computation with monotonic distortion reduction for two-dimensional convolution and frame-by-frame block-matching tasks. In order to reduce the run time of the proposed designs, we combine bitplane-based computation with a packing technique proposed recently. In the case of block matching, we also utilize previously-computed motion vectors to perform localized search when incrementing the precision of the input video frames. The applicability of the proposed approach is demonstrated by execution time measurements on the xo-laptop (ldquo100$ laptoprdquo) and on a mainstream laptop; our software is also made available online. In comparison to the conventional (non-incremental) software realization, the proposed approach leads to scalable computation per input frame while producing identical (or comparable) precision for the output results of each operating point. In addition, the execution of the proposed designs can be arbitrarily terminated for each frame with the output being available at the already-computed precision.
    Signal Processing Systems, 2009. SiPS 2009. IEEE Workshop on; 11/2009
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    Yiannis Andreopoulos
    IEEE Transactions on Image Processing 10/2009; 18(9):2143. DOI:10.1109/TIP.2009.2029190 · 3.11 Impact Factor
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    Y. Andreopoulos
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    ABSTRACT: In their recent paper, (see ibid., vol.17, no.7, p.1061-8, 2008) Alnasser and Foroosh derive a wavelet-domain (in-band) method for phase-shifting of 2-D ldquononseparablerdquo Haar transform coefficients. Their approach is parametrical to the (a priori known) image translation. In this correspondence, we show that the utilized transform is in fact the separable Haar discrete wavelet transform (DWT). As such, wavelet-domain phase shifting can be performed using previously-proposed phase-shifting approaches that utilize the overcomplete DWT (ODWT), if the given image translation is mapped to the phase component and in-band position within the ODWT.
    IEEE Transactions on Image Processing 09/2009; DOI:10.1109/TIP.2009.2021085 · 3.11 Impact Factor
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    N. Kontorinis, Y. Andreopoulos, M. van der Schaar
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    ABSTRACT: Video decoding complexity modeling and prediction is an increasingly important issue for efficient resource utilization in a variety of applications, including task scheduling, receiver-driven complexity shaping, and adaptive dynamic voltage scaling. In this paper we present a novel view of this problem based on a statistical framework perspective. We explore the statistical structure (clustering) of the execution time required by each video decoder module (entropy decoding, motion compensation, etc.) in conjunction with complexity features that are easily extractable at encoding time (representing the properties of each module's input source data). For this purpose, we employ Gaussian mixture models (GMMs) and an expectation-maximization algorithm to estimate the joint execution-time-feature probability density function (PDF). A training set of typical video sequences is used for this purpose in an offline estimation process. The obtained GMM representation is used in conjunction with the complexity features of new video sequences to predict the execution time required for the decoding of these sequences. Several prediction approaches are discussed and compared. The potential mismatch between the training set and new video content is addressed by adaptive online joint-PDF re-estimation. An experimental comparison is performed to evaluate the different approaches and compare the proposed prediction scheme with related resource prediction schemes from the literature. The usefulness of the proposed complexity-prediction approaches is demonstrated in an application of rate-distortion-complexity optimized decoding.
    IEEE Transactions on Circuits and Systems for Video Technology 08/2009; DOI:10.1109/TCSVT.2009.2020256 · 2.26 Impact Factor
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    Yiannis Andreopoulos, Ioannis Patras
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    ABSTRACT: Low-level image analysis systems typically detect "points of interest", i.e., areas of natural images that contain corners or edges. Most of the robust and computationally efficient detectors proposed for this task use the autocorrelation matrix of the localized image derivatives. Although the performance of such detectors and their suitability for particular applications has been studied in relevant literature, their behavior under limited input source (image) precision or limited computational or energy resources is largely unknown. All existing frameworks assume that the input image is readily available for processing and that sufficient computational and energy resources exist for the completion of the result. Nevertheless, recent advances in incremental image sensors or compressed sensing, as well as the demand for low-complexity scene analysis in sensor networks now challenge these assumptions. In this paper, we investigate an approach to compute salient points of images incrementally, i.e., the salient point detector can operate with a coarsely quantized input image representation and successively refine the result (the derived salient points) as the image precision is successively refined by the sensor. This has the advantage that the image sensing and the salient point detection can be terminated at any input image precision (e.g., bound set by the sensory equipment or by computation, or by the salient point accuracy required by the application) and the obtained salient points under this precision are readily available. We focus on the popular detector proposed by Harris and Stephens and demonstrate how such an approach can operate when the image samples are refined in a bitwise manner, i.e., the image bitplanes are received one-by-one from the image sensor. We estimate the required energy for image sensing as well as the computation required for the salient point detection based on stochastic source modeling. The computation and energy required by the proposed incremental refinement approach is compared against the conventional salient-point detector realization that operates directly on each source precision and cannot refine the result. Our experiments demonstrate the feasibility of incremental approaches for salient point detection in various classes of natural images. In addition, a first comparison between the results obtained by the intermediate detectors is presented and a novel application for adaptive low-energy image sensing based on points of saliency is presented.
    IEEE Transactions on Image Processing 10/2008; 17(9):1685-99. DOI:10.1109/TIP.2008.2001051 · 3.11 Impact Factor
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    Brian Foo, Yiannis Andreopoulos, Mihaela van der Schaar
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    ABSTRACT: Analytical modeling of the performance of video coders is essential in a variety of applications, such as power-constrained processing, complexity-driven video streaming, etc., where information concerning rate, distortion, or complexity (and their interrelation) is required. In this paper, we present a novel rate-distortion-complexity (R-D-C) analysis for state-of-the-art wavelet video coding methods by explicitly modeling several aspects found in operational coders, i.e., embedded quantization, quadtree decompositions of block significance maps and context-adaptive entropy coding of subband blocks. This paper achieves two main goals. First, unlike existing R-D models for wavelet video coders, the proposed derivations reveal for the first time the expected coding behavior of specific coding algorithms (e.g., quadtree decompositions, coefficient significance, and refinement coding) and, therefore, can be used for a variety of coding mechanisms incorporating some or all the coding algorithms discussed. Second, the proposed modeling derives for the first time analytical estimates of the expected number of operations (complexity) of a broad class of wavelet video coding algorithms based on stochastic source models, the coding algorithm characteristics and the system parameters. This enables the formulation of an analytical model characterizing the complexity of various video decoding operations. As a result, this paper complements prior complexity-prediction research that is based on operational measurements. The accuracy of the proposed analytical R-D-C expressions is justified against experimental data obtained with a state-of-the-art motion-compensated temporal filtering based wavelet video coder, and several new insights are revealed on the different tradeoffs between rate-distortion performance and the required decoding complexity.
    IEEE Transactions on Signal Processing 03/2008; DOI:10.1109/TSP.2007.906685 · 3.20 Impact Factor
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    Yiannis Andreopoulos, Mihaela van der Schaar
    [Show abstract] [Hide abstract]
    ABSTRACT: Contrary to the conventional paradigm of transform decomposition followed by quantization, we investigate the computation of two-dimensional (2D) discrete wavelet transforms (DWT) under quantized representations of the input source. The proposed method builds upon previous research on approximate signal processing and revisits the concept of incremental refinement of computation: Under a refinement of the source description (with the use of an embedded quantizer), the computation of the forward and inverse transform refines the previously computed result, thereby leading to incremental computation of the output. In the first part of this paper, we study both the forward and inverse DWT under state-of-the-art 2D lifting-based formulations. By focusing on conventional bitplane-based (double-deadzone) embedded quantization, we propose schemes that achieve incremental refinement of computation for the multilevel DWT decomposition or reconstruction based on a bitplane-by-bitplane calculation approach. In the second part, based on stochastic modeling of typical 2D DWT coefficients, we derive an analytical model to estimate the arithmetic complexity of the proposed incremental refinement of computation. The model is parameterized with respect to ( i) operational settings, such as the total number of decomposition levels and the terminating bitplane; (ii) input source and algorithm-related settings, e.g., the source variance, the complexity related to the choice of wavelet, etc. Based on the derived formulations, we study for which subsets of these model parameters the proposed framework derives identical reconstruction accuracy to the conventional approach without any incurring computational overhead. This is termed successive refinement of computation, since all representation accuracies are produced incrementally under a single (continuous) computation of the refined input source with no overhead in comparison to the conventional calculation approach that specifically ta- rgets each accuracy level and is not refinable. Our results, as well as the derived model estimates for incremental refinement, are validated with real video sequences compressed with a scalable coder.
    IEEE Transactions on Signal Processing 02/2008; DOI:10.1109/TSP.2007.906727 · 3.20 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The suitability of the 2D Discrete Wavelet Transform (DWT) as a tool in image and video compression is nowadays indisputable. For the execution of the multilevel 2D DWT, several computation schedules based on different input traversal patterns have been proposed. Among these, the most commonly used in practical designs are: the row–column, the line-based and the block-based. In this work, these schedules are implemented on FPGA-based platforms for the forward 2D DWT by using a lifting-based filter-bank implementation. Our designs were realized in VHDL and optimized in terms of throughput and memory requirements, in accordance with the principles of both the schedules and the lifting decomposition. The implementations are fully parameterized with respect to the size of the input image and the number of decomposition levels. We provide detailed experimental results concerning the throughput, the area, the memory requirements and the energy dissipation, associated with every point of the parameter space. These results demonstrate that the choice of the suitable schedule is a decision that should be dependent on the given algorithmic specifications.
    Journal of Signal Processing Systems 01/2008; 51(1):3-21. DOI:10.1007/s11265-007-0139-5 · 0.56 Impact Factor
  • Source
    Yiannis Andreopoulos, Mihaela van der Schaar
    [Show abstract] [Hide abstract]
    ABSTRACT: Contrary to the conventional paradigm of transform decomposition followed by quantization, we investigate the computation of two-dimensional discrete wavelet transforms (DWT) under quantized representations of the input source. The proposed method builds upon previous research on approximate signal processing and revisits the concept of incremental refinement of computation: Under a refinement of the source description (with the use of an embedded quantizer), the computation of the forward and inverse transform refines the previously-computed result thereby leading to incremental computation of the output. We study for which input sources (and computational-model parameters) can the proposed framework derive identical reconstruction accuracy to the conventional approach without any incurring computational overhead. This is termed successive refinement of computation, since all representation accuracies are produced incrementally under a single (continuous) computation of the refined input source and with no overhead in comparison to the conventional calculation approach that specifically targets each accuracy level and is not refinable.
  • Source
    Ioannis Patras, Yiannis Andreopoulos
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we investigate an approach that computes salient points, i.e. areas of natural images that contain corners or edges, incrementally. We focus on the popular Harris corner detector and demonstrate how such an approach can operate when the image samples are refined in a bitwise manner, i.e. the image bitplanes are received one-by-one from the image sensor. This has the advantage that the image sensing and the salient point detection can be terminated at any input image precision (e.g. at a bound set by the sensory equipment or by computation, or by the salient point accuracy required by the application) and the obtained salient points under this precision are readily available. We estimate the required energy for image sensing as well as the computation required for the salient point detection and compare them against the conventional salient point detector realization that operates directly on each source precision and cannot refine the result. Our experiments demonstrate the feasibility of incremental approaches for salient point detection in various classes of natural images. In addition, a first comparison between the results obtained by the intermediate detectors is presented.
    Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2008, March 30 - April 4, 2008, Caesars Palace, Las Vegas, Nevada, USA; 01/2008
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    Xiaolin Tong, Yiannis Andreopoulos, Mihaela van der Schaar
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    ABSTRACT: Multihop networks provide a flexible infrastructure that is based on a mixture of existing access points and stations interconnected via wireless links. These networks present some unique challenges for video streaming applications due to the inherent infrastructure unreliability. In this paper, we address the problem of robust video streaming in multihop networks by relying on delay- constrained and distortion-aware scheduling, path diversity, and retransmission of important video packets over multiple links to maximize the received video quality at the destination node. To provide an analytical study of this streaming problem, we focus on an elementary multihop network topology that enables path diversity, which we term "elementary cell." Our analysis is considering several cross-layer parameters at the physical and medium access control (MAC) layers, as well as application-layer parameters such as the expected distortion reduction of each video packet and the packet scheduling via an overlay network infrastructure. In addition, we study the optimal deployment of path diversity in order to cope with link failures. The analysis is validated in each case by simulation results with the elementary cell topology, as well as with a larger multihop network topology. Based on the derived results, we are able to establish the benefits of using path diversity in video streaming over multihop networks, as well as to identify the cases where path diversity does not lead to performance improvements.
    IEEE Transactions on Mobile Computing 01/2008; 6(12):1343-1356. DOI:10.1109/TMC.2007.1063 · 2.91 Impact Factor
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    Qiong Li, Yiannis Andreopoulos, Mihaela van der Schaar
    [Show abstract] [Hide abstract]
    ABSTRACT: State-of-the-art vehicles are now being equipped with multiple video channels for video-data transmission from multiple surveillance cameras mounted on the automobile, navigation videos reporting the traffic conditions on the planned route, as well as entertainment-multimedia streaming for passengers watching on rear-seat monitors. Wireless LANs provide a low-cost and flexible infrastructure for these emerging in-vehicle multimedia services aimed at the driver's and passengers' safety, convenience, and entertainment. To enable the successful simultaneous deployment of such applications over in-vehicle wireless networks, we propose delay-sensitive streaming and packet-scheduling algorithms that enable simple, flexible, and efficient adaptation of the video bitstreams to the instantaneously changing video source and wireless-channel characteristics while complying with the a priori negotiated quality-of-service (QoS) parameters for that video service. Our focus is on real-time low-cost solutions for multimedia transmission over in-vehicle wireless networks that are derived based on existing protocols defined by QoS-enabled networks, such as the IEEE 802.11e standard. In addition, the aim of this paper is to couple the proposed solutions with a novel multitrack-hinting method that is proposed as an extension of conventional MP4 hint tracks in order to provide real-time adaptation of multimedia streams to multiple quality levels for different in-vehicle applications, depending on their importance and delay constraints. First, the scheduling constraints for these simultaneous wireless video-streaming sessions are analytically expressed as a function of the negotiated QoS parameters. This is imperative because a video stream received from an in-vehicle road-surveillance camera will have a different set of delay and quality constraints in comparison to that of traffic monitoring received from remote video cameras located on the planned route. Hence, transmission paramete- - rs, such as peak data rate, maximum burst size, minimum transmission delay, maximum error rate, etc., will differ for the various video streams. For this reason, new low-complexity packet-scheduling algorithms that can fulfill diverse QoS streaming conditions are proposed and analyzed. The proposed algorithms produce viable schedules (i.e., strictly QoS-compliant) that jointly consider the delay constraints and the in-vehicle video-receiver-buffer conditions. Hence, these scheduling schemes can completely avoid the underflow or overflow event of the receiving-device buffer while guaranteeing the agreement between the real-time video traffic and the predetermined traffic specification reached during QoS negotiation for various in-vehicle video channels. When combined with multitrack hinting, an integrated flexible system for adaptive multimedia streaming over QoS-enabled in-vehicle wireless networks can be constructed. We demonstrate the viability of the proposed scheduling mechanisms experimentally by using real video traces under multiple quality levels, as derived by the multitrack-hinting design. In addition, simulations under realistic conditions are also performed to validate the ability of the method to satisfy buffer-occupancy constraints.
    IEEE Transactions on Vehicular Technology 12/2007; 56(6-56):3533 - 3549. DOI:10.1109/TVT.2007.901927 · 2.64 Impact Factor
  • Source
    Yiannis Andreopoulos, Mihaela van der Schaar
    [Show abstract] [Hide abstract]
    ABSTRACT: Current systems often assume "worst case" resource utilization for the design and implementation of compression techniques and standards, thereby neglecting the fact that multimedia coding algorithms require time-varying resources, which differ significantly from the "worst case" requirements. To enable adaptive resource management for multimedia systems, resource-estimation mechanisms are needed. Previous research demonstrated that online adaptive linear prediction techniques typically exhibit superior efficiency to other alternatives for resource prediction of multimedia systems. In this paper, we formulate the problem of adaptive linear prediction of video decoding resources by analytically expressing the possible adaptation parameters for a broad class of video decoders. The resources are measured in terms of the time required for a particular operation of each decoding unit (e.g., motion compensation or entropy decoding of a video frame). Unlike prior research that mainly focuses on estimation of execution time based on previous measurements (i.e., based on autoregressive prediction or platform and decoder-specific off-line training), we propose the use of generic complexity metrics (GCMs) as the input for the adaptive predictor. GCMs represent the number of times the basic building blocks are executed by the decoder and depend on the source characteristics, decoding bit rate, and the specific algorithm implementation. Different GCM granularities (e.g., per video frame or macroblock) are explored. Our previous research indicated that GCMs can be measured or modeled at the encoder or the video server side and they can be streamed to the decoder along with the compressed bitstream. A comparison of GCM-based versus autoregressive adaptive prediction over a large range of adaptation parameters is performed. Our results indicate that GCM-based prediction is significantly superior to the autoregressive approach and also requires less computational resources at the de- - coder. As a result, a novel resource-prediction tradeoff is explored between: 1) the communication overhead for GCMs and/or the implementation overhead for the realization of the predictor and 2) the improvement of the prediction performance. Since this tradeoff can be significant for the decoder platform (either from the communication or the implementation perspective), we propose complexity (or communication)-bounded adaptive linear prediction in order to derive the best resource estimation under the given implementation (or GCM-communication) bound
    IEEE Transactions on Circuits and Systems for Video Technology 07/2007; DOI:10.1109/TCSVT.2007.896662 · 2.26 Impact Factor

Publication Stats

981 Citations
134.12 Total Impact Points

Institutions

  • 2009–2013
    • University College London
      • Department of Electronic and Electrical Engineering
      Londinium, England, United Kingdom
  • 2008
    • University of London
      Londinium, England, United Kingdom
  • 2007–2008
    • Queen Mary, University of London
      Londinium, England, United Kingdom
  • 2006–2008
    • University of California, Los Angeles
      • Department of Electrical Engineering
      Los Ángeles, California, United States
  • 2005
    • University of California, Davis
      • Department of Electrical and Computer Engineering
      Davis, CA, United States
  • 2001–2005
    • Vrije Universiteit Brussel
      • Electronics and Informatics (ETRO)
      Bruxelles, Brussels Capital, Belgium