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Relevant bibliographies by topics / Multimedia signal processing / Journal articles

Journal articles on the topic 'Multimedia signal processing'

To see the other types of publications on this topic, follow the link: Multimedia signal processing.

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Cohen, Fernand S., Athina Petropulu, Georgia Georgiou, and Walid Ibrahim. "Multimedia digital signal processing laboratory." Computer Applications in Engineering Education 8, no. 3-4 (2000): 209–15. http://dx.doi.org/10.1002/1099-0542(2000)8:3/4<209::aid-cae12>3.0.co;2-e.

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2

Civanlar, R., and A. Reibman. "Signal processing for networked multimedia." IEEE Signal Processing Magazine 14, no. 4 (July 1997): 39–41. http://dx.doi.org/10.1109/79.598592.

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3

Wang, Xiaodong. "Advanced Signal Processing for Wireless Multimedia Communications." Informing Science: The International Journal of an Emerging Transdiscipline 3 (2000): 023–30. http://dx.doi.org/10.28945/572.

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4

Yen, G. G. "Editor's remarks - CI-Based multimedia signal processing." IEEE Computational Intelligence Magazine 1, no. 2 (May 2006): 2. http://dx.doi.org/10.1109/mci.2006.1626487.

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5

Emre, Yunus, and Chaitali Chakrabarti. "Energy and Quality-Aware Multimedia Signal Processing." IEEE Transactions on Multimedia 15, no. 7 (November 2013): 1579–93. http://dx.doi.org/10.1109/tmm.2013.2266094.

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6

Chou, Phil, Francesco De Natale, Enrico Magli, and Eckehard Steinbach. "Trends in Multimedia Signal Processing [In the Spotlight]." IEEE Signal Processing Magazine 28, no. 6 (November 2011): 197–98. http://dx.doi.org/10.1109/msp.2011.942320.

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7

NAKAYAMA, K. "Special Section on Multimedia and Mobile Signal Processing." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E90-A, no. 3 (March 1, 2007): 545. http://dx.doi.org/10.1093/ietfec/e90-a.3.545.

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8

Xu, Dong, Weisi Lin, and Anthony T. S. Ho. "Advances in Multimedia Content Analysis and Signal Processing." Journal of Signal Processing Systems 74, no. 1 (December 19, 2013): 1–3. http://dx.doi.org/10.1007/s11265-013-0866-8.

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9

Damiani, Ernesto, Albert Dipanda, and Andrea Kutics. "Guest editorial: online multimedia signal and image processing." Multimedia Tools and Applications 74, no. 19 (August 12, 2015): 8593–95. http://dx.doi.org/10.1007/s11042-015-2850-8.

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10

Han, Xiuqin. "Acquisition and its Basic Processing Technology of Multimedia Vocal Signal." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 08 (November 12, 2019): 2058009. http://dx.doi.org/10.1142/s0218001420580094.

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This paper briefly studies the method of collecting audio signals and the method of adding noise to audio signals. It comprehensively applies various basic knowledge of digital signal processing, and then performs spectrum analysis on noise-free frequency signals and spectral analysis of noise-added frequency signals, and filtering processing. Through theoretical derivation, the corresponding conclusions are drawn, and then MATLAB is used as a programming tool to carry out computer implementation to verify the conclusions derived. In the research process, the filter processing was completed by designing the IIR digital filter and the FIR digital filter, and MATLAB was used to draw the graphics and calculate and simulate some data in the whole design.

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11

Abboud, Ali J. "Shape Adaptable Medical Multimedia Processing." Open Electrical & Electronic Engineering Journal 13, no. 1 (January 31, 2019): 1–18. http://dx.doi.org/10.2174/1874129001913010001.

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Introduction:Electronic medical healthcare systems are becoming the backbone of health organizations over the world. The huge amounts of medical multimedia produced by these systems especially images and videos are transmitted by the computer networks that connect these systems. The variability in the shape and texture of transferred medical multimedia data needs adaptable procedures to process these data efficiently. In other words, these procedures must adjust automatically based on the shape of region of interests in the medical multimedia images to cope with fast changes in the healthcare environments. In this paper, we have proposed shape adaptable watermarking approaches for medical multimedia processing systems. The medical images generated by X-rays, MRI and CT modalities are used in our experiments to test proposed approaches. In addition, these approaches were tested under different kinds of signal processing and geometric attacks. The comparative comparison of our proposed approaches with state-of-art approaches proved the superiority and capability of our approaches to adjust the number of selected subands of medical cover image to embed and extract the hospital watermark logos.Background and Objective:The development of an adjustable approach to process medical multimedia signals for healthcare system. The aim of this research is to select adaptably the number of subands of cover image to hide the information of hospital logo watermark inside them such that embedded watermarks can resist different kinds of attacks.Method:The proposed adjustable approach consists of suband selection method, criterion, embedding and extraction procedures, DWT transform, attacks, evaluation metrics,etc.Results & Conclusion:It provides robust and adjustable method to embed and extract watermark logo at different resolution levels of cover medical image and uses with images of different sizes and modalities.

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12

Yu, Jun, Jitao Sang, and Xinbo Gao. "Machine learning and signal processing for big multimedia analysis." Neurocomputing 257 (September 2017): 1–4. http://dx.doi.org/10.1016/j.neucom.2017.01.091.

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13

Lingaiah, D. "An Interactive Multimedia Introduction to Signal Processing [Book Review]." IEEE Circuits and Devices Magazine 20, no. 5 (September 2004): 35. http://dx.doi.org/10.1109/mcd.2004.1343247.

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14

ITOH, Takashi, and Kiichi MATSUDA. "Precision Engineering for Multimedia Environment. Visual Signal Processing Technologies." Journal of the Japan Society for Precision Engineering 62, no. 2 (1996): 192–96. http://dx.doi.org/10.2493/jjspe.62.192.

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15

Kim, Byung-Gyu. "Digital Signal, Image and Video Processing for Emerging Multimedia Technology." Electronics 9, no. 12 (November 27, 2020): 2012. http://dx.doi.org/10.3390/electronics9122012.

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16

Liu, K. J. Ray, An-Yeu Wu, A. Raghupathy, and Jie Chen. "Algorithm-based low-power and high-performance multimedia signal processing." Proceedings of the IEEE 86, no. 6 (June 1998): 1155–202. http://dx.doi.org/10.1109/5.687834.

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17

Yinian Mao and Min Wu. "A joint signal processing and cryptographic approach to multimedia encryption." IEEE Transactions on Image Processing 15, no. 7 (July 2006): 2061–75. http://dx.doi.org/10.1109/tip.2006.873426.

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18

Asif, A. "Multimedia and Cooperative Learning in Signal Processing Techniques in Communications." IEEE Signal Processing Letters 11, no. 2 (February 2004): 278–81. http://dx.doi.org/10.1109/lsp.2003.821675.

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19

Rho, Seungmin, Damien Sauveron, and Weifeng Chen. "Advanced signal processing and HCI issues for interactive multimedia services." Multimedia Tools and Applications 73, no. 2 (September 17, 2013): 803–7. http://dx.doi.org/10.1007/s11042-013-1674-7.

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20

Atzori, Luigi, Jaime Delgado, and Daniele D. Giusto. "Signal processing: Image communication—Special issue on pervasive mobile multimedia." Signal Processing: Image Communication 27, no. 8 (September 2012): 785–87. http://dx.doi.org/10.1016/j.image.2012.05.002.

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21

Ghani, Hadhrami Ab, Mohamad Razwan Abdul Malek, Muhammad Fadzli Kamarul Azmi, Muhammad Jefri Muril, and Azizul Azizan. "A review on sparse fast fourier transform applications in image processing." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (April 1, 2020): 1346. http://dx.doi.org/10.11591/ijece.v10i2.pp1346-1351.

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Fast Fourier Transform has long been established as an essential tool in signal processing. To address the computational issues while helping the analysis work for multi-dimensional signals in image processing, sparse Fast Fourier Transform model is reviewed here when applied in different applications such as lithography optimization, cancer detection, evolutionary arts and wasterwater treatment. As the demand for higher dimensional signals in various applications especially multimedia appplications, the need for sparse Fast Fourier Transform grows higher.

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22

Troncoso-pastoriza, J. R., and F. Perez-Gonzalez. "Secure signal processing in the cloud: enabling technologies for privacy-preserving multimedia cloud processing." IEEE Signal Processing Magazine 30, no. 2 (March 2013): 29–41. http://dx.doi.org/10.1109/msp.2012.2228533.

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23

Chang Wen Chen. "Panorama of Multimedia Coding and Processing." IEEE MultiMedia 11, no. 3 (July 2004): 111–12. http://dx.doi.org/10.1109/mmul.2004.14.

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24

Mrak, Marta, Enrico Magli, and Frederic Dufaux. "Spotlight on the Multimedia Signal Processing Technical Committee [In the Spotlight]." IEEE Signal Processing Magazine 36, no. 3 (May 2019): 128–26. http://dx.doi.org/10.1109/msp.2019.2899243.

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25

Tekalp, A. M. "Special Issue On Multimedia Signal Processing, Part I [Scanning the Issue]." Proceedings of the IEEE 86, no. 5 (May 1998): 751–54. http://dx.doi.org/10.1109/jproc.1998.664271.

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26

Stolberg, Hans-Joachim, Mladen Bereković, Sören Moch, Lars Friebe, Mark B. Kulaczewski, Sebastian Flügel, Heiko Klußmann, Andreas Dehnhardt, and Peter Pirsch. "HiBRID-SoC: A Multi-Core SoC Architecture for Multimedia Signal Processing." Journal of VLSI signal processing systems for signal, image and video technology 41, no. 1 (August 2005): 9–20. http://dx.doi.org/10.1007/s11265-005-6247-1.

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27

Zeng, Zhi, Ning Tao, Li Chun Feng, and Cun Lin Zhang. "Research in Teaching Reform of Digital Signal Processing Course." Applied Mechanics and Materials 239-240 (December 2012): 1645–48. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1645.

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Digital signal processing is one important fundamental course for the students in the department of electrical engineering. The paper analyzes its teaching status based on the characteristics and problems in the teaching process of digital signal processing course. Combining the analysis and the personal teaching practice, the paper introduces how to reform the teaching methods and improve the quality in the course of digital signal processing, such as choosing a suitable textbook, inspiring students’ learning interest, organizing the teaching content reasonably, and combining multimedia and board writing, etc. Accordingly, students can better comprehend the basics of digital signal processing, and lay the foundation for further studies.

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28

Sikora, Thomas, and Hong Ren Wu. "Special issues on multimedia communication services." Circuits, Systems, and Signal Processing 20, no. 3-4 (May 2001): iii—vii. http://dx.doi.org/10.1007/bf01201402.

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29

Wang, Jin. "Research on the Multimedia Music Courseware Design Based on Internet Resource." Applied Mechanics and Materials 380-384 (August 2013): 2095–98. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2095.

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With the advance of computer information science and technology, multimedia courseware has been widely used in the application and development of teaching practice. The multimedia music courseware has good auditory dynamic guide and vividness. It can produce very good teaching effect in the teaching process. Music lessons belong to audio-visual vocal class teaching, the abstract of course makes music teaching need to develop multimedia dynamic courseware. Music lessons belong to audio-visual vocal class teaching, the abstract of course makes music teaching need to develop multimedia dynamic courseware. According to this, the paper studies the design and development of multimedia music courseware which treats the internet resources as a platform. According to the signal theory of digital audio and spectrum and smart composer of musical notation system software, this paper converts the audio to MIDI format. Then, it processes the audio signal of the data through Fourier audio signal processing model and gets the format which is suitable to the play of multimedia music courseware. Finally, this paper establishes the interaction of multimedia music courseware and Internet resources using Dreamweaver Web development software and achieves Internet access of multimedia music courseware which provides a theoretical reference for the design and development of multimedia music courseware.

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30

Furht, Borko. "Video and Image Processing in Multimedia Systems." Real-Time Imaging 2, no. 1 (February 1996): 1–2. http://dx.doi.org/10.1006/rtim.1996.0001.

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31

Kim, Sung-Ill, and Se-Jin Oh. "Virtual Dialog System Based on Multimedia Signal Processing for Smart Home Environments." Journal of Korean Institute of Intelligent Systems 15, no. 2 (April 1, 2005): 173–78. http://dx.doi.org/10.5391/jkiis.2005.15.2.173.

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32

Hunt, Andy, David M. Howard, Ross Kirk, Kingsley Ash, and Andy M. Tyrrell. "Interactive multimedia systems for engineering education in acoustics, synthesis and signal processing." European Journal of Engineering Education 26, no. 2 (June 2001): 91–106. http://dx.doi.org/10.1080/03043790110033574.

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33

Tsuhan Chen. "Guest Editor's Message [The Past, Present, and Future of Multimedia Signal Processing]." IEEE Signal Processing Magazine 14, no. 4 (July 1997): 29. http://dx.doi.org/10.1109/msp.1997.598584.

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34

Erkin, Zekeriya, Alessandro Piva, Stefan Katzenbeisser, R. L. Lagendijk, Jamshid Shokrollahi, Gregory Neven, and Mauro Barni. "Protection and Retrieval of Encrypted Multimedia Content: When Cryptography Meets Signal Processing." EURASIP Journal on Information Security 2007 (2007): 1–20. http://dx.doi.org/10.1155/2007/78943.

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35

Erkin, Zekeriya, Alessandro Piva, Stefan Katzenbeisser, RL Lagendijk, Jamshid Shokrollahi, Gregory Neven, and Mauro Barni. "Protection and Retrieval of Encrypted Multimedia Content: When Cryptography Meets Signal Processing." EURASIP Journal on Information Security 2007, no. 1 (2007): 078943. http://dx.doi.org/10.1186/1687-417x-2007-078943.

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36

Stolberg, Hans-Joachim, Mladen Bereković, and Peter Pirsch. "A Platform-Independent Methodology for Performance Estimation of Multimedia Signal Processing Applications." Journal of VLSI signal processing systems for signal, image and video technology 41, no. 2 (September 2005): 139–51. http://dx.doi.org/10.1007/s11265-005-6646-3.

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37

Goljan, Miroslav, and Andreas Westfeld. "Secure Steganography in Multimedia Content." EURASIP Journal on Information Security 2009 (2009): 1. http://dx.doi.org/10.1155/2009/257131.

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38

Aho, Eero, Jarno Vanne, and Timo D. HÄmÄlÄinen. "Configurable Data Memory for Multimedia Processing." Journal of Signal Processing Systems 50, no. 2 (August 16, 2007): 231–49. http://dx.doi.org/10.1007/s11265-007-0126-x.

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39

K., Mythili, and Manish Narwaria. "Assessment of Machine Learning-Based Audiovisual Quality Predictors." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 2 (June 2021): 1–22. http://dx.doi.org/10.1145/3430376.

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Quality assessment of audiovisual (AV) signals is important from the perspective of system design, optimization, and management of a modern multimedia communication system. However, automatic prediction of AV quality via the use of computational models remains challenging. In this context, machine learning (ML) appears to be an attractive alternative to the traditional approaches. This is especially when such assessment needs to be made in no-reference (i.e., the original signal is unavailable) fashion. While development of ML-based quality predictors is desirable, we argue that proper assessment and validation of such predictors is also crucial before they can be deployed in practice. To this end, we raise some fundamental questions about the current approach of ML-based model development for AV quality assessment and signal processing for multimedia communication in general. We also identify specific limitations associated with the current validation strategy which have implications on analysis and comparison of ML-based quality predictors. These include a lack of consideration of: (a) data uncertainty, (b) domain knowledge, (c) explicit learning ability of the trained model, and (d) interpretability of the resultant model. Therefore, the primary goal of this article is to shed some light into mentioned factors. Our analysis and proposed recommendations are of particular importance in the light of significant interests in ML methods for multimedia signal processing (specifically in cases where human-labeled data is used), and a lack of discussion of mentioned issues in existing literature.

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40

Pan, Jeng-Shyang, and Peng Shi. "Guest Editorial: Digital Watermarking and Multimedia Security." Circuits, Systems & Signal Processing 27, no. 2 (March 6, 2008): 133–36. http://dx.doi.org/10.1007/s00034-008-9018-y.

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41

FENG, DAGAN (DAVID). "CONTENT-BASED RETRIEVAL OF MULTIMEDIA INFORMATION." International Journal of Image and Graphics 01, no. 01 (January 2001): 83–91. http://dx.doi.org/10.1142/s0219467801000074.

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The recent information explosion has led to massively increased demand for multimedia data storage and retrieval techniques. Content-based retrieval is an important alternative and complement to the traditional keyword-based searching for multimedia data and can greatly enhance information management. For the last ten years, the Biomedical and Multimedia Information Technology (BMIT) Group and recently the Center for Multimedia Signal Processing (CMSP) have conducted systematic studies and research activities on this topic. Some of the works relating to content-based image/video retrieval and their applications are briefly presented in this paper.

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42

Sarkar, Mrinmoy, Dhiman Chowdhury, Celia Shahnaz, and Shaikh Anowarul Fattah. "Application of Electrical Network Frequency of Digital Recordings for Location-Stamp Verification." Applied Sciences 9, no. 15 (August 2, 2019): 3135. http://dx.doi.org/10.3390/app9153135.

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Electrical network frequency (ENF) is a signature of a power distribution grid. It represents the deviation from the nominal frequency (50 or 60 Hz) of a power system network. The variations in ENF sequences within a grid are subject to load fluctuations within that particular grid. These ENF variations are inherently located in a multimedia signal, which is recorded close to the grid or directly from the mains power line. Thus, the specific location of a recording can be identified by analyzing the ENF sequences of the multimedia signal in absence of the concurrent power signal. In this article, a novel approach to location-stamp authentication based on ENF sequences of digital recordings is presented. ENF patterns are extracted from a number of power and audio signals recorded in different grid locations across the world. The extracted ENF signals are decomposed into low outliers and high outliers frequency segments and potential feature vectors are determined for these ENF segments by statistical and signal processing analysis. Then, a multi-class support vector machine (SVM) classification model is developed to verify the location-stamp information of the recordings. The performance evaluations corroborate the efficacy of the proposed framework.

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43

Ye, Xi En, and Xing Chen. "Video Signal Capture and Processing Card Based on H.264 Hardware Encoder." Applied Mechanics and Materials 128-129 (October 2011): 745–48. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.745.

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This paper proposes a hardware and software design proposal of video collection and processing used in digital multimedia microteaching system. The system core is H.264 hardware encoder. 4-channel video data in the case of D1 (720*576) @25fps can be encoded in real-time and uploaded to the PC in distance control room. On the premise of stability, the system adopts friendly operating interface, the user operation is very convenience.

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44

Zhanfeng, LIU. "Research on the Shot Put Technique of College Athletes based on Network Multimedia Teaching." International Journal of Signal Processing, Image Processing and Pattern Recognition 9, no. 4 (April 30, 2016): 69–80. http://dx.doi.org/10.14257/ijsip.2016.9.4.07.

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45

Cheung, Sen-ching Samson, Deepa Kundur, and Andrew Senior. "Enhancing Privacy Protection in Multimedia Systems." EURASIP Journal on Information Security 2009 (2009): 1–2. http://dx.doi.org/10.1155/2009/710919.

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46

Perkis, Andrew, Yousri Abdeljaoued, Charilaos Christopoulos, Touradj Ebrahimi, and Joe F. Chicharo. "Universal multimedia access from wired and wireless systems." Circuits, Systems, and Signal Processing 20, no. 3-4 (May 2001): 387–402. http://dx.doi.org/10.1007/bf01201409.

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47

Trappe, W., Min Wu, Z. J. Wang, and K. J. Ray Liu. "Anti-collusion fingerprinting for multimedia." IEEE Transactions on Signal Processing 51, no. 4 (April 2003): 1069–87. http://dx.doi.org/10.1109/tsp.2003.809378.

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48

Ramkumar, M., and A. N. Akansu. "Signaling Methods for Multimedia Steganography." IEEE Transactions on Signal Processing 52, no. 4 (April 2004): 1100–1111. http://dx.doi.org/10.1109/tsp.2004.823468.

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49

Candan, K. Selçuk, Jong Wook Kim, Parth Nagarkar, Mithila Nagendra, and Renwei Yu. "RanKloud: Scalable Multimedia Data Processing in Server Clusters." IEEE Multimedia 18, no. 1 (January 2011): 64–77. http://dx.doi.org/10.1109/mmul.2010.70.

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50

Amatriain, X. "A Domain-Specific Metamodel for Multimedia Processing Systems." IEEE Transactions on Multimedia 9, no. 6 (October 2007): 1284–98. http://dx.doi.org/10.1109/tmm.2007.902885.

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