| 1. |
- Du, Xu, et al.
(författare)
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Joint Replication Density and Rate Allocation Optimization for VoD Systems over Wireless Mesh Networks
- 2013
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Ingår i: IEEE transactions on circuits and systems for video technology (Print). - : IEEE. - 1051-8215 .- 1558-2205. ; 23:7, s. 1260-1273
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Tidskriftsartikel (refereegranskat)abstract
- Due to the limited resources and dynamically varying nature of wireless links, guaranteeing high quality demands for a large number of heterogeneous users is very challenging in video streaming over wireless networks. In this paper, we introduce a layered multiple description coding with an embedded forward error correction scheme (LMDC-FEC). The combination of layered MDC and FEC aims at coping with not only the diverse bandwidth and unreliability of wireless links but also the heterogeneous user devices. We further propose a joint replication density and rate allocation (RD-RA) optimization problem in the context of video on-demand systems (VoDs) over wireless mesh networks (WMNs), and employ a genetic algorithms based approach to solve the optimization problem. Our objective is to elaborately distribute proper replication density for each video segment and allocate optimal bit rate to each layer of each segment in order to gain high userperceived quality (UPQ) with small consumption of storage resource. By this method, both optimal replication densities and rate allocations are found in accordance with the access rate of segments, the diverse loss characteristics of descriptions in each segment, and the rate–distortion relationship of segments, so as to thoroughly optimize UPQ and storage resource consumption. Simulation results demonstrate that our proposed method significantly enhances the streaming performance of VoDs over WMNs.
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| 2. |
- Eriksson, Emil, et al.
(författare)
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Coordinating Distributed Algorithms for Feature Extraction Offloading in Multi-Camera Visual Sensor Networks
- 2018
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Ingår i: IEEE transactions on circuits and systems for video technology (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1051-8215 .- 1558-2205. ; 28:11, s. 3288-3299
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Tidskriftsartikel (refereegranskat)abstract
- Real-time visual analysis tasks, like tracking and recognition, require swift execution of computationally intensive algorithms. Visual sensor networks could be enabled to perform such tasks by allowing the camera nodes to offload their computational load to nearby processing nodes. In this paper, we address the problem of minimizing the completion time of multiple camera sensors that share the transmission and the processing resources of multiple processing nodes for computation offloading. We show that the problem is NP-hard, and propose a combination of central coordination and distributed optimization with limited signaling among the camera sensors as a solution. We analyze the existence of equilibrium allocations for the distributed algorithms, evaluate the effect of the network topology and of the video characteristics on the algorithms' performance, and assess the benefits of central coordination. Our results demonstrate that with sufficient information available, distributed optimization can provide low completion times, moreover predictable and stable performance can be achieved with additional, sparse central coordination.
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| 3. |
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| 5. |
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| 6. |
- Garus, P., et al.
(författare)
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Immersive Video Coding : Should Geometry Information be Transmitted as Depth Maps?
- 2021
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Ingår i: IEEE transactions on circuits and systems for video technology (Print). - : Institute of Electrical and Electronics Engineers Inc.. - 1051-8215 .- 1558-2205.
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Tidskriftsartikel (refereegranskat)abstract
- Immersive video often refers to multiple views with texture and scene geometry information, from which different viewports can be synthesized on the client side. To design efficient immersive video coding solutions, it is desirable to minimize bitrate, pixel rate and complexity. We investigate whether the classical approach of sending the geometry of a scene as depth maps is appropriate to serve this purpose. Previous work shows that bypassing depth transmission entirely and estimating depth at the client side improves the synthesis performance while saving bitrate and pixel rate. In order to understand if the encoder side depth maps contain information that is beneficial to be transmitted, we first explore a hybrid approach which enables partial depth map transmission using a block-based RD-based decision in the depth coding process. This approach reveals that partial depth map transmission may improve the rendering performance but does not present a good compromise in terms of compression efficiency. This led us to address the remaining drawbacks of decoder side depth estimation: complexity and depth map inaccuracy. We propose a novel system that takes advantage of high quality depth maps at the server side by encoding them into lightweight features that support the depth estimator at the client side. These features allow reducing the amount of data that has to be handled during decoder side depth estimation by 88%, which significantly speeds up the cost computation and the energy minimization of the depth estimator. Furthermore, -46.0% and -37.9% average synthesis BD-Rate gains are achieved compared to the classical approach with depth maps estimated at the encoder. IEEE
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| 7. |
- Imran, Muhammad, et al.
(författare)
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Energy Efficient SRAM FPGA based Wireless Vision Sensor Node: SENTIOF‐CAM
- 2014
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Ingår i: IEEE transactions on circuits and systems for video technology (Print). - 1051-8215 .- 1558-2205. ; 24:12, s. 2132-2143
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Tidskriftsartikel (refereegranskat)abstract
- Many Wireless Vision Sensor Networks (WVSNs) applications are characterized to have a low duty cycling. An individual wireless Vision Senor Node (VSN) in WVSN is required to operate with limited resources i.e., processing, memory and wireless bandwidth on available limited energy. For such resource constrained VSN, this paper presents a low complexity, energy efficient and programmable VSN architecture based on a design matrix which includes partitioning of processing load between the node and a server, a low complexity background subtraction, bi-level video coding and duty cycling. The tasks partitioning and proposed background subtraction reduces the processing energy and design complexity for hardware implemented VSN. The bi-level video coding reduces the communication energy whereas the duty cycling conserves energy for lifetime maximization. The proposed VSN, referred to as SENTIOF-CAM, has been implemented on a customized single board, which includes SRAM FPGA, microcontroller, radio transceiver and a FLASH memory. The energy values are measured for different states and results are compared with existing solutions. The comparison shows that the proposed solution can offer up to 69 times energy reduction. The lifetime based on measured energy values shows that for a sample period of 5 minutes, a 3.2 years lifetime can be achieved with a battery of 37.44 kJ energy. In addition to this, the proposed solution offers generic architecture with smaller design complexity on a hardware reconfigurable platform and offers easy adaptation for a number of applications.
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| 8. |
- Imran, Muhammad, et al.
(författare)
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Implementation of wireless Vision Sensor Node for Characterization of Particles in Fluids
- 2012
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Ingår i: IEEE transactions on circuits and systems for video technology (Print). - 1051-8215 .- 1558-2205. ; 22:11, s. 1634-1643
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Tidskriftsartikel (refereegranskat)abstract
- Wireless Vision Sensor Networks (WVSNs) have a number of wireless Vision Sensor Nodes (VSNs), often spread over a large geographical area. Each node has an image capturing unit, a battery or alternative energy source, a memory unit, a light source, a wireless link and a processing unit. The challenges associated with WVSNs include low energy consumption, low bandwidth, limited memory and processing capabilities. In order to meet these challenges, our research is focused on the exploration of energy efficient reconfigurable architectures for VSN. In this work, the design/research challenges associated with the implementation of VSN on different computational platforms such as micro-controller, FPGA and server, are explored. In relation to this, the effect on the energy consumption and the design complexity at the node, when the functionality is moved from one platform to another are analyzed. Based on the implementation of the VSN on embedded platforms, the lifetime of the VSN is predicted using the measured energy values of the platforms for different implementation strategies. The implementation results show that an architecture, where the compressed images after pixel based operation are transmitted, realize a WVSN system with low energy consumption. Moreover, the complex post processing tasks are moved to a server, with reduced constraints.
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| 9. |
- Karlsson, Linda Sofia, 1976-, et al.
(författare)
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Layer assignment based on depth data distribution for multiview-plus-depth scalable video coding
- 2011
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Ingår i: IEEE transactions on circuits and systems for video technology (Print). - 1051-8215 .- 1558-2205. ; 21:6, s. 742-754
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Tidskriftsartikel (refereegranskat)abstract
- Three dimensional (3D) video is experiencing a rapid growth in a number of areas including 3D cinema, 3DTV and mobile phones. Several problems must to be addressed to display captured 3D video at another location. One problem is how torepresent the data. The multiview plus depth representation of a scene requires a lower bit rate than transmitting all views required by an application and provides more information than a 2D-plus-depth sequence. Another problem is how to handle transmission in a heterogeneous network. Scalable video coding enables adaption of a 3D video sequence to the conditions at the receiver. In this paper we present a scheme that combines scalability based on the position in depth of the data and the distance to the center view. The general scheme preserves the center view data, whereas the data of the remaining views are extracted in enhancement layers depending on distance to the viewer and the center camera. The data is assigned into enhancement layers within a view based on depth data distribution. Strategies concerning the layer assignment between adjacent views are proposed. In general each extracted enhancement layer increases the visual quality and PSNR compared to only using center view data. The bit-rate per layer can be further decreased if depth data is distributed over the enhancement layers. The choice of strategy to assign layers between adjacent views depends on whether quality of the fore-most objects in the scene or the quality of the views close to the center is important.
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| 10. |
- Keserwani, Prateek, et al.
(författare)
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Robust Scene Text Detection for Partially Annotated Training Data
- 2022
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Ingår i: IEEE transactions on circuits and systems for video technology (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1051-8215 .- 1558-2205. ; 32:12, s. 8635-8645
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Tidskriftsartikel (refereegranskat)abstract
- This article analyzed the impact of training data containing un-annotated text instances, i.e., partial annotation in scene text detection, and proposed a text region refinement approach to address it. Scene text detection is a problem that has attracted the attention of the research community for decades. Impressive results have been obtained for fully supervised scene text detection with recent deep learning approaches. These approaches, however, need a vast amount of completely labeled datasets, and the creation of such datasets is a challenging and time-consuming task. Research literature lacks the analysis of the partial annotation of training data for scene text detection. We have found that the performance of the generic scene text detection method drops significantly due to the partial annotation of training data. We have proposed a text region refinement method that provides robustness against the partially annotated training data in scene text detection. The proposed method works as a two-tier scheme. Text-probable regions are obtained in the first tier by applying hybrid loss that generates pseudo-labels to refine text regions in the second-tier during training. Extensive experiments have been conducted on a dataset generated from ICDAR 2015 by dropping the annotations with various drop rates and on a publicly available SVT dataset. The proposed method exhibits a significant improvement over the baseline and existing approaches for the partially annotated training data.
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