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  • 1.
    Al-Zubaidy, Hussein
    et al.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Fodor, Viktoria
    KTH, School of Electrical Engineering (EES), Network and Systems engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Dán, György
    KTH, School of Electrical Engineering (EES), Network and Systems engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Flierl, Markus
    KTH, School of Electrical Engineering (EES), Information Science and Engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Reliable Video Streaming With Strict Playout Deadline in Multihop Wireless Networks2017In: IEEE transactions on multimedia, ISSN 1520-9210, E-ISSN 1941-0077, Vol. 19, no 10, p. 2238-2251Article in journal (Refereed)
    Abstract [en]

    Motivated by emerging vision-based intelligent services, we consider the problem of rate adaptation for high-quality and low-delay visual information delivery over wireless networks using scalable video coding. Rate adaptation in this setting is inherently challenging due to the interplay between the variability of the wireless channels, the queuing at the network nodes, and the frame-based decoding and playback of the video content at the receiver at very short time scales. To address the problem, we propose a low-complexity model-based rate adaptation algorithm for scalable video streaming systems, building on a novel performance model based on stochastic network calculus. We validate the analytic model using extensive simulations. We show that it allows fast near-optimal rate adaptation for fixed transmission paths, as well as cross-layer optimized routing and video rate adaptation in mesh networks, with less than 10% quality degradation compared to the best achievable performance.

  • 2.
    Dán, György
    et al.
    KTH, School of Electrical Engineering (EES), Communication Networks. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Khan, Muhammadaltamash A.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Fodor, Viktoria
    KTH, School of Electrical Engineering (EES), Communication Networks. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Characterization of SURF and BRISK interest point distribution for distributed feature extraction in visual sensor networks2015In: IEEE transactions on multimedia, ISSN 1520-9210, E-ISSN 1941-0077, Vol. 17, no 5, p. 591-602, article id 7047857Article in journal (Refereed)
    Abstract [en]

    We study the statistical characteristics of SURF and BRISK interest points and descriptors, with the aim of supporting the design of distributed processing across sensor nodes in a resource -constrained visual sensor network (VSN). Our results show high variability in the density, the spatial distribution , and the octave layer distribution of the interest points. The high variability implies that balancing the processing load among the sensor nodes is a very challenging task, and obtaining a priori information is essential, e.g., through prediction. Our results show that if a priori information is available about the images, then Top-$M$ interest point selection, limited , octave-based processing at the camera node, together with area-based interest point detection and extraction at the processing nodes, can balance the processing load and limit the transmission cost in the network. Complete interest point detection at the camera node with optimized descriptor extraction delegation to the processing nodes in turn can further decrease the transmission load and allow a better balance of the processing load among the network nodes.

  • 3.
    Kozica, Ermin
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Kleijn, W. Bastiaan
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    On Bandwidth-Efficient Video Distribution Through Multi-Rate Video Encoding2011In: IEEE transactions on multimedia, ISSN 1520-9210, E-ISSN 1941-0077Article in journal (Other academic)
  • 4.
    Plasberg, Jan H.
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Kleijn, W. Bastiaan
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Feature Selection Under a Complexity Constraint2009In: IEEE transactions on multimedia, ISSN 1520-9210, E-ISSN 1941-0077, Vol. 11, no 3, p. 565-571Article in journal (Refereed)
    Abstract [en]

    Classification on mobile devices is often done in an uninterrupted fashion. This requires algorithms with gentle demands on the computational complexity. The performance of a classifier depends heavily on the set of features used as input variables. Existing feature selection strategies for classification aim at finding a "best" set of features that performs well in terms of classification accuracy, but are not designed to handle constraints on the computational complexity. We demonstrate that an extension of the performance measures used in state-of-the-art feature selection algorithms with a penalty on the feature extraction complexity leads to superior feature sets if the allowed computational complexity is limited. Our solution is independent of a particular classification algorithm.

  • 5.
    Vukadinovic, Vladimir
    et al.
    KTH, School of Electrical Engineering (EES), Communication Networks.
    Karlsson, Gunnar
    KTH, School of Electrical Engineering (EES), Communication Networks.
    Trade-Offs in Bit-Rate Allocation for Wireless Video Streaming2009In: IEEE transactions on multimedia, ISSN 1520-9210, E-ISSN 1941-0077, Vol. 11, no 6, p. 1105-1113Article in journal (Refereed)
    Abstract [en]

    One of the central problems in wireless video transmission is the choice of source and channel coding rates to allocate the available transmission rate optimally. In this paper, we present a structural distortion model for video streaming over time-varying fading channels. Based on the model, we study the end-to-end distortion for various bit-rate allocation strategies and channel conditions. We show that the robustness to channel variations is crucial for the streaming performance when frequent bit-rate adaptations are not feasible. It is achieved at the expense of higher source distortion in the encoder. Our findings are illustrated on a practical problem of distortion-optimal selection of transport formats in an adaptive modulation and coding (AMC) scheme used in HSDPA.

  • 6. Yan, Jingjie
    et al.
    Zheng, Wenming
    Xu, Qinyu
    Lu, Guanming
    Li, Haibo
    KTH, School of Computer Science and Communication (CSC), Media Technology and Interaction Design, MID. Nanjing Univ Posts & Telecommun, Peoples R China.
    Wang, Bei
    Sparse Kernel Reduced-Rank Regression for Bimodal Emotion Recognition From Facial Expression and Speech2016In: IEEE transactions on multimedia, ISSN 1520-9210, E-ISSN 1941-0077, Vol. 18, no 7, p. 1319-1329Article in journal (Refereed)
    Abstract [en]

    A novel bimodal emotion recognition approach from facial expression and speech based on the sparse kernel reduced-rank regression (SKRRR) fusion method is proposed in this paper. In this method, we use the openSMILE feature extractor and the scale invariant feature transform feature descriptor to respectively extract effective features from speech modality and facial expression modality, and then propose the SKRRR fusion approach to fuse the emotion features of two modalities. The proposed SKRRR method is a nonlinear extension of the traditional reduced-rank regression (RRR), where both predictor and response feature vectors in RRR are kernelized by being mapped onto two high-dimensional feature space via two nonlinear mappings, respectively. To solve the SKRRR problem, we propose a sparse representation (SR)-based approach to find the optimal solution of the coefficient matrices of SKRRR, where the introduction of the SR technique aims to fully consider the different contributions of training data samples to the derivation of optimal solution of SKRRR. Finally, we utilize the eNTERFACE '05 and AFEW4.0 bimodal emotion database to conduct the experiments of monomodal emotion recognition and bimodal emotion recognition, and the results indicate that our presented approach acquires the highest or comparable bimodal emotion recognition rate among some state-of-the-art approaches.

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