Relevant bibliographies by topics / Multimedia signal processing

Academic literature on the topic 'Multimedia signal processing'

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Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Multimedia signal processing"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Multimedia signal processing"

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Athanasiadis, Tasso, and tas atha@bigpond net au. "Signal Processing Techniques for Mobile Multimedia Systems." RMIT University. Electrical and Computer Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080123.115457.

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Recent trends in wireless communication systems show a significant demand for the delivery of multimedia services and applications over mobile networks - mobile multimedia - like video telephony, multimedia messaging, mobile gaming, interactive and streaming video, etc. However, despite the ongoing development of key communication technologies that support these applications, the communication resources and bandwidth available to wireless/mobile radio systems are often severely limited. It is well known, that these bottlenecks are inherently due to the processing capabilities of mobile transmission systems, and the time-varying nature of wireless channel conditions and propagation environments. Therefore, new ways of processing and transmitting multimedia data over mobile radio channels have become essential which is the principal focus of this thesis. In this work, the performance and suitability of various signal processing techniques and transmission strategies in the application of multimedia data over wireless/mobile radio links are investigated. The proposed transmission systems for multimedia communication employ different data encoding schemes which include source coding in the wavelet domain, transmit diversity coding (space-time coding), and adaptive antenna beamforming (eigenbeamforming). By integrating these techniques into a robust communication system, the quality (SNR, etc) of multimedia signals received on mobile devices is maximised while mitigating the fast fading and multi-path effects of mobile channels. To support the transmission of high data-rate multimedia applications, a well known multi-carrier transmission technology known as Orthogonal Frequency Division Multiplexing (OFDM) has been implemented. As shown in this study, this results in significant performance gains when combined with other signal-processing techniques such as spa ce-time block coding (STBC). To optimise signal transmission, a novel unequal adaptive modulation scheme for the communication of multimedia data over MIMO-OFDM systems has been proposed. In this system, discrete wavelet transform/subband coding is used to compress data into their respective low-frequency and high-frequency components. Unlike traditional methods, however, data representing the low-frequency data are processed and modulated separately as they are more sensitive to the distortion effects of mobile radio channels. To make use of a desirable subchannel state, such that the quality (SNR) of the multimedia data recovered at the receiver is optimized, we employ a lookup matrix-adaptive bit and power allocation (LM-ABPA) algorithm. Apart from improving the spectral efficiency of OFDM, the modified LM-ABPA scheme, sorts and allocates subcarriers with the highest SNR to low-frequency data and the remaining to the least important data. To maintain a target system SNR, the LM-ABPA loading scheme assigns appropriate signal constella tion sizes and transmit power levels (modulation type) across all subcarriers and is adapted to the varying channel conditions such that the average system error-rate (SER/BER) is minimised. When configured for a constant data-rate load, simulation results show significant performance gains over non-adaptive systems. In addition to the above studies, the simulation framework developed in this work is applied to investigate the performance of other signal processing techniques for multimedia communication such as blind channel equalization, and to examine the effectiveness of a secure communication system based on a logistic chaotic generator (LCG) for chaos shift-keying (CSK).
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Guo, Liwei. "Restoration and modeling for multimedia compression /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?ECED%202008%20GUOL.

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Bakken, Marianne. "Signal Processing for Communicating Gravity Wave Images from the NTNU Test Satellite." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elektronikk og telekommunikasjon, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-19229.

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The NTNU Test Satellite (NUTS) is planned to have a payload for observation of atmospheric gravity waves. The gravity waves will be observed by means of an infrared camera imaging the perturbations in the OH airglow layer. So far, no suitable camera has been found that complies with the restrictions that follows when building a small satellite. Uncooled InGaAs has however been concluded to be the most suitable detector type in terms of wavelength response and weight.InGaAs sensors are known to have a high dark current when not cooled, and processing must therefore be applied to remove the background offset and noise.The combination of the high speed of the satellite and the long exposure time that is required for the camera will create motion blur. Simulations with synthetic test images in MATLAB showed that the integration time should at least be kept under 1 second in order not to destroy the wave patterns. Longer integration times may however be required in order to get a sufficient SNR.Two signal processing solutions to this problem was investigated: motion blur removal by deconvolution and image averaging with motion compensation. The former strategy is to apply a long exposure time to get a strong signal, and then remove the blur with deconvolution techniques using knowledge of the blur filter.Simulations applying the Lucy-Richardson (LR) algorithm showed that it was not able to remove strong blur, and was very sensitive to errors in the blur filter and noise in the image. The other approach is to obtain a sequence of images with short exposure time in order to avoid motion blur, and provide the necessary SNR by shifting the images according to the known motion and combine them into one image. This concept is simpler and more reliable than the deconvolution approach, and simulations showed that it is less sensitive to errors in the speed estimate than the deconvolution algorithm. It was concluded that this is the most suitable approach for the NUTS application, and it should be implemented on-board the satellite in order to provide a good SNR for the compression to function optimally. The downlink datarate of NUTS is of only 9600 bit/s, and it has been estimated that 2.45 Mb of payload data can be downloaded on average per day. This corresponds to less than 5 uncompressed images of 256 × 256 pixels with 8 bit per pixel.A sequence of overlapping combined images should be obtained to provide a scan of a desired area, and it was suggested that it should be encoded as video to enable efficient compression and transmission of as many images as possible to the ground station. A three-dimensional DPCM algorithm combined with a deadzone quantizer and stack-run coding was implemented in MATLAB. Simulations demonstrated that this simple compression scheme can provide a bit rate of less than 1 bit/px for a sequence of ravity wave images. One of the quantizers that was tried gave 0.83 bits per pixel with reasonable quality. If this number can be achieved in practice, the image transfer ate would be increased to 45 images per day, which is a significant improvement.
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Houas, Heykel. "Allocation de ressources pour la transmission de données multimedia scalables." Phd thesis, Université de Cergy Pontoise, 2009. http://tel.archives-ouvertes.fr/tel-00767889.

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Cette thèse s'intéresse aux problèmes d'allocation de ressources pour la transmission de données multimédia scalables sous contraintes de qualité de service (QoS) sur les réseaux hétérogènes. Les liaisons filaires et sans fil considérées (DS-CDMA, OFDMA) sont appliquées à des services de transmission d'images et de parole sur des canaux à évanouissements lents ou rapides, avec ou sans multitrajets. La QoS de ces réseaux est exprimée en terme de qualité perçue du point de vue de l'utilisateur (couche Application) et en terme de taux d'erreurs binaires (TEB) par classe du point de vue de la transmission (couche Physique). Les ressources étudiées sont : l'allocation des puissances, des ordres de modulation et des porteuses ainsi que les propriétés de protection inégale contre les erreurs (UEP). L'objectif de ce document est d'allouer ces ressources de façon à maximiser le débit source des données multimédia hiérarchisées (sous forme de classes d'importance) en s'appuyant sur une connaissance parfaite ou partielle des canaux de propagation, sous contrainte de performances cibles en réception. Les stratégies d'adaptation de lien que nous présentons se basent sur la possible troncature d'une partie de ces données à transmettre. Elles se fondent également sur le degré de sensibilité et la protection adéquate de chacune de ces classes contre les erreurs liées à la transmission sur le canal, conformément aux exigences de QoS exprimées sur ces dernières. Les schémas de transmission explorent plusieurs critères d'optimisation des ressources : la minimisation de la charge utile du système ainsi que l'optimisation de la robustesse de la transmission aux erreurs d'estimation du canal. Dans ces contextes, nous décrivons l'allocation optimale de sous-porteuses, de modulations, de rendements de code et d'énergie maximisant le débit source de l'utilisateur tout en véri ant les contraintes sur la charge du système et la QoS. Nous montrons que ces schémas d'allocation sont adaptables à de nombreux systèmes de communication et présentent des performances supérieures aux stratégies de l'état de l'art.
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Oberhofer, Robert. "Pitch adaptive variable bitrate CELP speech coding." Thesis, University of Ulster, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264811.

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DeBardelaben, James Anthony. "An optimization-based approach for cost-effective embedded DSP system design." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/15757.

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Shen, Ju. "Computational Multimedia for Video Self Modeling." UKnowledge, 2014. http://uknowledge.uky.edu/cs_etds/26.

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Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of oneself. This is the idea behind the psychological theory of self-efficacy - you can learn or model to perform certain tasks because you see yourself doing it, which provides the most ideal form of behavior modeling. The effectiveness of VSM has been demonstrated for many different types of disabilities and behavioral problems ranging from stuttering, inappropriate social behaviors, autism, selective mutism to sports training. However, there is an inherent difficulty associated with the production of VSM material. Prolonged and persistent video recording is required to capture the rare, if not existed at all, snippets that can be used to string together in forming novel video sequences of the target skill. To solve this problem, in this dissertation, we use computational multimedia techniques to facilitate the creation of synthetic visual content for self-modeling that can be used by a learner and his/her therapist with a minimum amount of training data. There are three major technical contributions in my research. First, I developed an Adaptive Video Re-sampling algorithm to synthesize realistic lip-synchronized video with minimal motion jitter. Second, to denoise and complete the depth map captured by structure-light sensing systems, I introduced a layer based probabilistic model to account for various types of uncertainties in the depth measurement. Third, I developed a simple and robust bundle-adjustment based framework for calibrating a network of multiple wide baseline RGB and depth cameras.
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Sezer, Osman Gokhan. "Data-driven transform optimization for next generation multimedia applications." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42765.

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The objective of this thesis is to formulate a generic dictionary learning method with the guiding principle that states: Efficient representations lead to efficient estimations. The fundamental idea behind using transforms or dictionaries for signal representation is to exploit the regularity within data samples such that the redundancy of the representation is minimized subject to a level of fidelity. This observation translates to rate-distortion cost in compression literature, where a transform that has the lowest rate-distortion cost provides a more efficient representation than the others. In our work, rather than using as an analysis tool, the rate-distortion cost is utilized to improve the efficiency of transforms. For this, an iterative optimization method is proposed, which seeks an orthonormal transform that reduces the expected value of rate-distortion cost of an ensemble of data. Due to the generic nature of the new optimization method, one can design a set of orthonormal transforms either in the original signal domain or on the top of a transform-domain representation. To test this claim, several image codecs are designed, which use block-, lapped- and wavelet-transform structures. Significant increases in compression performances are observed compared to original methods. An extension of the proposed optimization method for video coding gave us state-of-the-art compression results with separable transforms. Also using the robust statistics, an explanation to the superiority of new design over other learning-based methods such as Karhunen-Loeve transform is provided. Finally, the new optimization method and the minimization of the "oracle" risk of diagonal estimators in signal estimation is shown to be equal. With the design of new diagonal estimators and the risk-minimization-based adaptation, a new image denoising algorithm is proposed. While these diagonal estimators denoise local image patches, by formulation the optimal fusion of overlapping local denoised estimates, the new denoising algorithm is scaled to operate on large images. In our experiments, the state-of-the-art results for transform-domain denoising are achieved.
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Narapareddy, Yagna Brahma Sai. "QoE Performance Evaluation by Introducing Video Freeze on Mobile Multimedia." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18995.

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Real time video streaming through mobile Internet is increasing day by day and the video  quality can be affected  very badly by network performance issues. Video freezing and video  jumping are one of the serious issues that affect the user experience badly. Hence service providers are interested to evaluate the performance of quality of experience. We  follow the methods from the International Telecommunications Union–Telecommunication Sector(ITU-T)recommendations. In this thesis, we are studying the effect of freezing on user experience by subjective tests and obtaining the mean opinion scores using perceptual video quality assessment tool and analyze  which part of the video is affected mostly by introducing freezein selected parts.
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Uzuegbunam, Nkiruka M. A. "SELF-IMAGE MULTIMEDIA TECHNOLOGIES FOR FEEDFORWARD OBSERVATIONAL LEARNING." UKnowledge, 2018. https://uknowledge.uky.edu/ece_etds/124.

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This dissertation investigates the development and use of self-images in augmented reality systems for learning and learning-based activities. This work focuses on self- modeling, a particular form of learning, actively employed in various settings for therapy or teaching. In particular, this work aims to develop novel multimedia systems to support the display and rendering of augmented self-images. It aims to use interactivity (via games) as a means of obtaining imagery for use in creating augmented self-images. Two multimedia systems are developed, discussed and analyzed. The proposed systems are validated in terms of their technical innovation and their clinical efficacy in delivering behavioral interventions for young children on the autism spectrum.
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Books on the topic "Multimedia signal processing"

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Kim, Tai-hoon, Sankar K. Pal, William I. Grosky, Niki Pissinou, Timothy K. Shih, and Dominik Ślęzak, eds. Signal Processing and Multimedia. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17641-8.

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Wang, Fu Lee, Jingsheng Lei, Rynson W. H. Lau, and Jingxin Zhang, eds. Multimedia and Signal Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35286-7.

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Signal Processing First: A multimedia approach. 2nd ed. Upper Saddle River, New Jersey: Prentice Hall, 2008.

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Wysocki, Tadeusz A., Bahram Honary, and Beata J. Wysocki, eds. Signal Processing for Telecommunications and Multimedia. Boston, MA: Springer US, 2005. http://dx.doi.org/10.1007/b99846.

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Damiani, Ernesto, Kokou Yétongnon, Peter Schelkens, Albert Dipanda, Louis Legrand, and Richard Chbeir, eds. Signal Processing for Image Enhancement and Multimedia Processing. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-72500-0.

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W, Schafer Ronald, and Yoder M. A, eds. Signal processing first. [Hemel Hempstead]: Pearson Education, 2003.

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1938-, Schafer Ronald W., and Yoder M. A, eds. Signal processing first. Upper Saddle River, NJ: Pearson/Prentice Hall, 2003.

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Karrenberg, Ulrich. An Interactive Multimedia Introduction to Signal Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04949-5.

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1938-, Schafer Ronald W., and Yoder M. A, eds. DSP first: A multimedia approach. Upper Saddle River, NJ: Prentice Hall, 1998.

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Grgić, Mislav. Recent Advances in Multimedia Signal Processing and Communications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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Book chapters on the topic "Multimedia signal processing"

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Mouchtaris, A., J. S. Lim, T. Holman, and C. Kyriakakis. "Signal Processing Considerations for Immersive Audio Rendering." In Multimedia Communications, 57–70. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0859-7_5.

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Yang, Jun. "Wireless Video Measurement Based on Its Gabor Samples in High-Dimension Space." In Multimedia and Signal Processing, 291–300. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35286-7_37.

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Chen, Jian Mei. "The Improved Partition Entropy Coefficient." In Multimedia and Signal Processing, 1–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35286-7_1.

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Tan, Sieow Yeek, and Dickson Lukose. "Cognitive Semantic Model for Visual Object Recognition in Image." In Multimedia and Signal Processing, 67–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35286-7_10.

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Zhao, Qian, Jialin Cao, and Yueli Hu. "Image Retrieval Based on Color-Spatial Distributing Feature." In Multimedia and Signal Processing, 79–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35286-7_11.

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Moydin, Kamil, and Askar Hamdulla. "Wavelet Domain Distributed Information Entropy and Genetic Clustering Algorithm for Image Retrieval." In Multimedia and Signal Processing, 87–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35286-7_12.

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Lv, Jin-jian, Gang Deng, and Bo Xu. "A New Automated Image Registration Method Based on Corners." In Multimedia and Signal Processing, 95–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35286-7_13.

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Li, Yuanjiang, and Yuehua Li. "Symmetric Normal Inverse Gaussian and Structural Similarity Based Image Denoising." In Multimedia and Signal Processing, 103–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35286-7_14.

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Zhang, Yi, Xin Jiang, Zhen-Dong Wang, and Jiang Yue. "An Improved Edge Detection Method Based on Composite Morphology." In Multimedia and Signal Processing, 112–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35286-7_15.

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Wang, Shi, Long Ye, Wei Zhong, and Qin Zhang. "Image Compression Based on Hierarchical Clustering Vector Quantization." In Multimedia and Signal Processing, 120–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35286-7_16.

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Conference papers on the topic "Multimedia signal processing"

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Lin, Yu-Hsun. "3D multimedia signal processing." In the 20th ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2393347.2396515.

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"Digital signal processing and multimedia." In 2015 12th International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services (TELSIKS). IEEE, 2015. http://dx.doi.org/10.1109/telsks.2015.7357734.

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"Session 22 overview multimedia signal processing." In 2002 IEEE International Solid-State Circuits Conference. Digest of Technical Papers. IEEE, 2002. http://dx.doi.org/10.1109/isscc.2002.993083.

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Cemgil, A. Taylan. "Bayesian methods for multimedia signal processing." In the 15th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1291233.1291235.

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Richard, Gaël. "Tutorial on multimedia music signal processing." In the 19th ACM international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2072298.2072402.

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De Figueiredo, R. J. "Intelligent signal processing for multimedia communications." In Seventh IEEE International Symposium on Multimedia. IEEE, 2005. http://dx.doi.org/10.1109/ism.2005.70.

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Huang, X., and G. A. Woolsey. "Multimedia materials for teaching signal processing." In IEEE International Conference on Multimedia and Expo, 2001. ICME 2001. IEEE, 2001. http://dx.doi.org/10.1109/icme.2001.1237885.

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"Proceedings of First Signal Processing Society Workshop on Multimedia Signal Processing." In Proceedings of First Signal Processing Society Workshop on Multimedia Signal Processing. IEEE, 1997. http://dx.doi.org/10.1109/mmsp.1997.602603.

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Luican, Ilie I., Hongwei Zhu, and Florin Balasa. "Signal-to-Memory Mapping Analysis for Multimedia Signal Processing." In 2007 Asia and South Pacific Design Automation Conference. IEEE, 2007. http://dx.doi.org/10.1109/aspdac.2007.358033.

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"KASKADA – MULTIMEDIA PROCESSING PLATFORM ARCHITECTURE." In International Conference on Signal Processing and Multimedia Applications. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002944800260031.

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