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Articoli di riviste sul tema "Scalable video"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 9 marzo 2023

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1

Wang, Te-heng, Mei-juan Chen, Ming-chieh Chi, Shu-fen Huang, and Chia-hung Yeh. "Computation-scalable algorithm for scalable video coding." IEEE Transactions on Consumer Electronics 57, no. 3 (August 2011): 1194–202. http://dx.doi.org/10.1109/tce.2011.6018874.

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2

Hu Chen, Meng-Ping Kao, and Truong Q. Nguyen. "Bidirectional Scalable Motion for Scalable Video Coding." IEEE Transactions on Image Processing 19, no. 11 (November 2010): 3059–64. http://dx.doi.org/10.1109/tip.2010.2050933.

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3

Höferlin, Benjamin, Markus Höferlin, Gunther Heidemann, and Daniel Weiskopf. "Scalable video visual analytics." Information Visualization 14, no. 1 (June 5, 2013): 10–26. http://dx.doi.org/10.1177/1473871613488571.

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Abstract (sommario):

Video visual analytics is the research field that addresses scalable and reliable analysis of video data. The vast amount of video data in typical analysis tasks renders manual analysis by watching the video data impractical. However, automatic evaluation of video material is not reliable enough, especially when it comes to semantic abstraction from the video signal. In this article, we describe the video visual analytics method that combines the complementary strengths of human recognition and machine processing. After inspecting the challenges of scalable video analysis, we derive the main components of visual analytics for video data. Based on these components, we present our video visual analytics system that has its origins in our IEEE VAST Challenge 2009 participation.

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4

Alhaisoni, Majed, Mohammed Ghanbari, and Antonio Liotta. "Scalable P2P Video Streaming." International Journal of Business Data Communications and Networking 6, no. 3 (July 2010): 49–65. http://dx.doi.org/10.4018/jbdcn.2010070103.

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P2P networks are a technology able to deliver real time and video-on-demand services over IP networks. Layered video coding techniques are being introduced due to their ability to deliver streams at different scales (temporal, spatial and SNR) that solve the heterogeneity problem. This eases transmission in the case of limited bandwidth, as the devices can pick and decode the minimum bit rate base layer. Existing work examines layered video in client-server scenarios. In contrast, this paper analyzes scalable coding H.264/SVC over P2P networks based on an SNR-temporal Codec. Due to the interdependency between the different SVC layers, issues of reliability and quality of experience arise unless proper measures are taken to protect the base layer. The authors explore the effectiveness of a combination of P2P strategies, for example, hybrid P2P architecture, P2P locality, and P2P redundancy, to assess the viability and benefits of scalable video coding over P2P. The resulting performance is compared with a state-of-the-art P2P TV platform.

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5

Jiang Li, Keman Yu, Tielin He, Yunfeng Lin, Shipeng Li, and Ya-Qin Zhang. "Scalable portrait video for mobile video communication." IEEE Transactions on Circuits and Systems for Video Technology 13, no. 5 (May 2003): 376–84. http://dx.doi.org/10.1109/tcsvt.2003.811611.

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6

Schierl, T., T. Stockhammer, and T. Wiegand. "Mobile Video Transmission Using Scalable Video Coding." IEEE Transactions on Circuits and Systems for Video Technology 17, no. 9 (September 2007): 1204–17. http://dx.doi.org/10.1109/tcsvt.2007.905528.

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7

Hou, Yanzhao, Nan Hu, Qimei Cui, and Xiaofeng Tao. "Performance analysis of scalable video transmission in machine-type-communication caching network." International Journal of Distributed Sensor Networks 15, no. 1 (January 2019): 155014771881585. http://dx.doi.org/10.1177/1550147718815851.

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In this article, different from the traditional Device-to-Device caching wireless cellular networks, we consider the scalable video coding performance in cache-based machine-type communication network, where popular videos encoded by scalable video coding method can be cached at machine-type devices with limited memory space. We conduct a comprehensive analysis of the caching hit probability using stochastic geometry, which measures the probability of requested video files cached by nearby local devices and the user satisfaction index, which is essential to delay sensitive video streams. Simulation results prove the derivation of the performance metrics to be correct, using Random cache method and Popularity Priority cache method. It is also demonstrated that scalable video coding–based caching method can be applied according to different user requirements as well as video-type requests, to achieve a better performance.

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8

Secker, A., and D. Taubman. "Highly scalable video compression with scalable motion coding." IEEE Transactions on Image Processing 13, no. 8 (August 2004): 1029–41. http://dx.doi.org/10.1109/tip.2004.826089.

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9

Rantelobo, Kalvein, Hendro Lami, and Wirawan Wirawan. "Video Transmission using Combined Scalability Video Coding over MIMO-OFDM Systems." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 2 (November 1, 2016): 390. http://dx.doi.org/10.11591/ijeecs.v4.i2.pp390-396.

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<p><em>The needs of efficient bandwidth utilization and method to handle bandwidth fluctuation condition of wireless channel have become fundamental problems in video transmission. This research</em><em> proposed </em><em>Combined Scalable Video Coding (CSVC) that refers to Joint Scalable Video Model (JSVM), i.e. development of video coding H.264/AVC by exploiting scalable combination method using Medium Grain Scalability (MGS) on wireless channel of MIMO-OFDM (Multiple Input Multiple Output – Orthogonal Frequency Division Multiplexing) technology. Th</em><em>e</em><em> research shows that the scalable combination method can be implemented on the scenario for wireless transmission on multicast network. Experimental results show</em><em> </em><em>that the delivered quality is close to the alternative traditional simulcast delivery mechanism in MIMO-OFDM systems.</em></p>

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10

Feng, Wei, Ashraf A. Kassim, and Chen-Khong Tham. "A scalable video codec for layered video streaming." Real-Time Imaging 10, no. 5 (October 2004): 297–305. http://dx.doi.org/10.1016/j.rti.2004.08.005.

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11

Dawa, Mohamed, and Faten Ben Abdallah. "FPGA Implementation of the IEEE 802.16 Physical Layer for SHVC Video Transmission." Journal of Circuits, Systems and Computers 27, no. 12 (June 22, 2018): 1850190. http://dx.doi.org/10.1142/s0218126618501906.

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This paper presents a video transmission system for scalable High-Efficiency Video Coding (HEVC) videos using a 4G standard’s physical layer. SHVC, the scalable HEVC is used to compress the different layers of videos into binary files. The resultant binary files are easily transportable over any network thus solving many issues mainly related to videos with high resolutions. Three scenarios are studied and simulated at first, namely, Single Input Single Output (SISO), Multiple Input Single Output (MISO) and MIMO. Since the MIMO scenario offers the best results, it is considered in the implementation of the system on Field Programmable Gate Array (FPGA) using Xilinx System Generator (XSG). A Simulink model is developed under Matlab to simulate the video transmission scenarios using the WIMAX physical layer. Then, the MIMO system is implemented using a Zed-Board to co-simulate the video transmission in real-time and which allows a successful reception of the video sequences.

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12

Liu, Yan, Xinheng Wang, and Liqiang Zhao. "Scalable Video Streaming in Wireless Mesh Networks for Education." International Journal of Distance Education Technologies 9, no. 1 (January 2011): 1–20. http://dx.doi.org/10.4018/jdet.2011010101.

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In this paper, a video streaming system for education based on a wireless mesh network is proposed. A wireless mesh network is a self-organizing, self-managing and reliable intelligent network, which allows educators to deploy a network quickly. Video streaming plays an important role in this system for multimedia data transmission. This new system adopts the scalable video coding scheme that enables the video server to deliver layered videos to different user groups. In addition, a quality control method was developed to automatically change the output data rate based on network conditions. Real implementation test results show the proposed methods are effective.

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13

Meng-Ping Kao and Truong Nguyen. "A Fully Scalable Motion Model for Scalable Video Coding." IEEE Transactions on Image Processing 17, no. 6 (June 2008): 908–23. http://dx.doi.org/10.1109/tip.2008.921307.

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14

Ohm, J. R. "Advances in Scalable Video Coding." Proceedings of the IEEE 93, no. 1 (January 2005): 42–56. http://dx.doi.org/10.1109/jproc.2004.839611.

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15

Kimoto, Takahiro. "H.264/Scalable Video Coding." Journal of The Institute of Image Information and Television Engineers 61, no. 4 (2006): 422–25. http://dx.doi.org/10.3169/itej.61.422.

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16

Gonzales, Cesar, and Eric Viscito. "Flexible scalable digital video coding." Signal Processing: Image Communication 5, no. 1-2 (February 1993): 5–20. http://dx.doi.org/10.1016/0923-5965(93)90024-n.

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17

Girod, B. "Scalable video for multimedia workstations." Computers & Graphics 17, no. 3 (May 1993): 269–76. http://dx.doi.org/10.1016/0097-8493(93)90075-k.

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18

Deljavan Amiri, Mehran, Ali Amiri, and Majid Meghdadi. "HVS-based scalable video watermarking." Multimedia Systems 25, no. 4 (January 30, 2019): 273–91. http://dx.doi.org/10.1007/s00530-019-00604-0.

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19

Shang, Yueyun, Dengpan Ye, Zhuo Wei, and Yajuan Xie. "GPU-Based MPEG-2 to Secure Scalable Video Transcoding." International Journal of Digital Crime and Forensics 6, no. 2 (April 2014): 52–69. http://dx.doi.org/10.4018/ijdcf.2014040104.

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Abstract (sommario):

Most of the high definition video content are still produced in a single-layer MPEG-2 format. Multiple-layers Scalable Video Coding (SVC) offers a minor penalty in rate-distortion efficiency when compared to single-layer coding MPEG-2. A scaled version of the original SVC bitstream can easily be extracted by dropping layers from the bitstream. This paper proposes a parallel transcoder from MPEG-2 to SVC video with Graphics Processing Unit (GPU), named PTSVC. The objective of the transcoder is to migrate MPEG-2 format video to SVC format video such that clients with different network bandwidth and terminal devices can seamlessly access video content. Meanwhile, the transcoded SVC videos are encrypted such that only authorized users can access corresponding SVC layers. Using various scalabilities SVC test sequences, experimental results on TM5 and JSVM indicate that PTSVC is a higher efficient transcoding system compared with previous systems and only causes little quality loss.

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20

Sakthivelan, R. G., P. Rajendran, and M. Thangavel. "An Accurate Efficient and Scalable Event Based Video Search Method Using Spectral Clustering." Journal of Computational and Theoretical Nanoscience 15, no. 2 (February 1, 2018): 537–41. http://dx.doi.org/10.1166/jctn.2018.7118.

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Web mining discovers enormous set of data and gets hidden and valuable information which contains text, images, audio and video files from the web search engine which is software that provides a significant result of information. Video rehabilitation for the context gives efficient comprehension of the video content. Video retrieval refers to the task of retrieving most relevant videos from the video Search engine but the outcome listed result could not achieve the relevant videos according to the user needs. This paper addresses Event based Video Retrieval (EBVR) uses metadata, which gives the accurate result. The aim is detect the circumstances of a focal point such as birthday party. In order to overcome this issue, we proposed a personalization approach which captures the user query relevance to their event. Video preprocessing method used to extract related precision data and spectral clustering technique for Video Categorization which yields event extraction and contributes associated video.

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21

Parakh, Shreyans, and Aditya K. Jagannatham. "Optimal Resource Allocation and VCG Auction-Based Pricing for H.264 Scalable Video Quality Maximization in 4G Wireless Systems." Advances in Multimedia 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/567217.

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We present novel schemes for optimal OFDMA bitrate allocation towards video quality maximization in H.264 scalable video coding (SVC)-based 4G wireless systems. We use the rate and quality models for video characterization of the SVC extension of the H.264/AVC and develop the framework for optimal scalable video transmission. Subsequently, we derive the closed form solution of the optimal H.264 scalable video quantization parameter for sum video quality maximization in unicast and multicast 4G WiMAX adaptive modulation and coding (AMC) scenarios. We also formulate a Vickrey-Clarke-Groves (VCG) auction-based time-frequency (TF) resource pricing scheme for dynamic bitrate allocation and simultaneous prevention of video quality degradation by malicious users for H.264-based scalable video transmission. Simulation results demonstrate that application of the proposed optimal 4G OFDMA schemes for unicast/multicast video quality maximization yield significantly superior performance in comparison to fixed rate video agnostic allocation.

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22

Reddyvari, Vamseedhar R., and Aditya K. Jagannatham. "Optimal H.264 Scalable Video Scheduling Policies for 3G/4G Wireless Cellular and Video Sensor Networks." Advances in Multimedia 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/207471.

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We consider the problem of optimal H.264 scalable video scheduling, with an objective of maximizing the end-user video quality while ensuring fairness in 3G/4G broadband wireless networks and video sensor networks. We propose a novel framework to characterize the video quality-based utility of the H.264 temporal and quality scalable video layers. Subsequently, we formulate the scalable video scheduling framework as a Markov decision process (MDP) for long-term average video utility maximization and derive the optimal index based-scalable video scheduling policies ISVP and ISVPF towards video quality maximization. Further, we extend this framework to multiuser and multisubchannel scenario of 4G wireless networks. In this context, we propose two novel schemes for long-term streaming video quality performance optimization based on maximum weight bipartite and greedy matching paradigms. Simulation results demonstrate that the proposed algorithms achieve superior end-user video experience compared to competing scheduling policies such as Proportional Fairness (PF), Linear Index Policy (LIP), Rate Starvation Age policy (RSA), and Quality Proportional Fair Policy (QPF).

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23

YANG, Hong, Linbo QING, Xiaohai HE, and Shuhua XIONG. "Scalable Distributed Video Coding for Wireless Video Sensor Networks." IEICE Transactions on Information and Systems E101.D, no. 1 (2018): 20–27. http://dx.doi.org/10.1587/transinf.2017mup0006.

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24

Yoo, Ha-Na, Cheon-Seog Kim, Ho-Jun Lee, Sung-Ho Jin, and Yong-Man Ro. "Quality Metric with Video Characteristics on Scalable Video Coding." Journal of Broadcast Engineering 13, no. 2 (March 31, 2008): 179–87. http://dx.doi.org/10.5909/jbe.2008.13.2.179.

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25

Park, Un-Ki, Haechul Choi, Jung Kang, and Jae-Gon Kim. "Scalable video coding with large block for UHD video." IEEE Transactions on Consumer Electronics 58, no. 3 (August 2012): 932–40. http://dx.doi.org/10.1109/tce.2012.6311339.

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26

López-Fuentes, F. A. "P2P Video Streaming Strategies based on Scalable Video Coding." Journal of Applied Research and Technology 13, no. 1 (February 2015): 113–24. http://dx.doi.org/10.1016/s1665-6423(15)30010-9.

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27

Morand, Cl, J. Benois-Pineau, J. Ph Domenger, J. Zepeda, E. Kijak, and Ch Guillemot. "Scalable object-based video retrieval in HD video databases." Signal Processing: Image Communication 25, no. 6 (July 2010): 450–65. http://dx.doi.org/10.1016/j.image.2010.04.004.

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28

Diez, J. M., and V. Casares. "Traffic model for scalable and non scalable MPEG VBR Video." IEEE Latin America Transactions 3, no. 3 (July 2005): 242–47. http://dx.doi.org/10.1109/tla.2005.1642414.

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29

Grois, Dan, and Ofer Hadar. "Efficient Region-of-Interest Scalable Video Coding with Adaptive Bit-Rate Control." Advances in Multimedia 2013 (2013): 1–17. http://dx.doi.org/10.1155/2013/281593.

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Abstract (sommario):

This work relates to the regions-of-interest (ROI) coding that is a desirable feature in future applications based on the scalable video coding, which is an extension of the H.264/MPEG-4 AVC standard. Due to the dramatic technological progress, there is a plurality of heterogeneous devices, which can be used for viewing a variety of video content. Devices such as smartphones and tablets are mostly resource-limited devices, which make it difficult to display high-quality content. Usually, the displayed video content contains one or more ROI(s), which should be adaptively selected from the preencoded scalable video bitstream. Thus, an efficient scalable ROI video coding scheme is proposed in this work, thereby enabling the extraction of the desired regions-of-interest and the adaptive setting of the desirable ROI location, size, and resolution. In addition, an adaptive bit-rate control is provided for the region-of-interest scalable video coding. The performance of the presented techniques is demonstrated and compared with the joint scalable video model reference software (JSVM 9.19), thereby showing significant bit-rate savings as a tradeoff for the relatively low PSNR degradation.

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30

Figueroa, A. Morales, and L. Favalli. "Buffer management for scalable video streaming." ICST Transactions on Mobile Communications and Applications 3, no. 8 (September 13, 2017): 153337. http://dx.doi.org/10.4108/eai.13-9-2017.153337.

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31

HAYASE, Kazuya, Hiroshi FUJII, Yukihiro BANDOH, and Hirohisa JOZAWA. "Recent Advances on Scalable Video Coding." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E95.A, no. 8 (2012): 1230–39. http://dx.doi.org/10.1587/transfun.e95.a.1230.

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32

Reibman, A. R., L. Bottou, and A. Basso. "Scalable video coding with managed drift." IEEE Transactions on Circuits and Systems for Video Technology 13, no. 2 (February 2003): 131–40. http://dx.doi.org/10.1109/tcsvt.2002.808435.

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33

Macnicol, J., J. Arnold, and M. Frater. "Scalable video coding by stream morphing." IEEE Transactions on Circuits and Systems for Video Technology 15, no. 2 (February 2005): 306–19. http://dx.doi.org/10.1109/tcsvt.2004.841692.

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34

Amon, P., T. Rathgen, and D. Singer. "File Format for Scalable Video Coding." IEEE Transactions on Circuits and Systems for Video Technology 17, no. 9 (September 2007): 1174–85. http://dx.doi.org/10.1109/tcsvt.2007.905521.

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35

Yongjun Wu, K. Hanke, T. Rusert, and J. W. Woods. "Enhanced MC-EZBC Scalable Video Coder." IEEE Transactions on Circuits and Systems for Video Technology 18, no. 10 (October 2008): 1432–36. http://dx.doi.org/10.1109/tcsvt.2008.927003.

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36

Eleftheriadis, Alexandros, M. Reha Civanlar, and Ofer Shapiro. "Multipoint videoconferencing with scalable video coding." Journal of Zhejiang University-SCIENCE A 7, no. 5 (May 2006): 696–705. http://dx.doi.org/10.1631/jzus.2006.a0696.

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37

Shi, Zhongbo, Xiaoyan Sun, and Feng Wu. "Spatially Scalable Video Coding For HEVC." IEEE Transactions on Circuits and Systems for Video Technology 22, no. 12 (December 2012): 1813–26. http://dx.doi.org/10.1109/tcsvt.2012.2223031.

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38

Moon, Yong Ho. "A Fast Scalable Video Encoding Algorithm." IEMEK Journal of Embedded Systems and Applications 7, no. 5 (October 31, 2012): 285–90. http://dx.doi.org/10.14372/iemek.2012.7.5.285.

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39

Wiegand, T., L. Noblet, and F. Rovati. "Scalable Video Coding for IPTV Services." IEEE Transactions on Broadcasting 55, no. 2 (June 2009): 527–38. http://dx.doi.org/10.1109/tbc.2009.2020954.

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40

Horn, Uwe, and Bernd Girod. "Scalable video transmission for the Internet." Computer Networks and ISDN Systems 29, no. 15 (November 1997): 1833–42. http://dx.doi.org/10.1016/s0169-7552(97)00093-7.

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Radha, Hayder, Yingwei Chen, Kavitha Parthasarathy, and Robert Cohen. "Scalable Internet video using MPEG-4." Signal Processing: Image Communication 15, no. 1-2 (September 1999): 95–126. http://dx.doi.org/10.1016/s0923-5965(99)00026-0.

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Waschbüsch, Michael, Stephan Würmlin, Daniel Cotting, Filip Sadlo, and Markus Gross. "Scalable 3D video of dynamic scenes." Visual Computer 21, no. 8-10 (August 31, 2005): 629–38. http://dx.doi.org/10.1007/s00371-005-0346-7.

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Saikia, Navajit, and Prabin K. Bora. "Perceptual hash function for scalable video." International Journal of Information Security 13, no. 1 (September 15, 2013): 81–93. http://dx.doi.org/10.1007/s10207-013-0211-z.

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Adami, Nicola, Alberto Boschetti, Riccardo Leonardi, and Pierangelo Migliorati. "Embedded indexing in scalable video coding." Multimedia Tools and Applications 48, no. 1 (September 23, 2009): 105–21. http://dx.doi.org/10.1007/s11042-009-0356-y.

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Kangas, Tero, Timo D. Hämäläinen, and Kimmo Kuusilinna. "Scalable Architecture for SoC Video Encoders." Journal of VLSI signal processing systems for signal, image and video technology 44, no. 1-2 (May 27, 2006): 79–95. http://dx.doi.org/10.1007/s11265-006-5918-x.

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Leontaris, A., and P. C. Cosman. "Drift-resistant SNR scalable video coding." IEEE Transactions on Image Processing 15, no. 8 (August 2006): 2191–97. http://dx.doi.org/10.1109/tip.2006.877412.

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Naman, A. T., and D. Taubman. "JPEG2000-Based Scalable Interactive Video (JSIV)." IEEE Transactions on Image Processing 20, no. 5 (May 2011): 1435–49. http://dx.doi.org/10.1109/tip.2010.2093905.

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Singh, A., J. Bove, and V. Mkhael. "Multidimensional quantizers for scalable video compression." IEEE Journal on Selected Areas in Communications 11, no. 1 (1993): 36–45. http://dx.doi.org/10.1109/49.210542.

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Ed Chang and A. Zakhor. "Disk-based storage for scalable video." IEEE Transactions on Circuits and Systems for Video Technology 7, no. 5 (1997): 758–70. http://dx.doi.org/10.1109/76.633494.

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Ke Shen and E. J. Delp. "Wavelet based rate scalable video compression." IEEE Transactions on Circuits and Systems for Video Technology 9, no. 1 (1999): 109–22. http://dx.doi.org/10.1109/76.744279.

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