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Cambridge University Press 978-0-521-76451-3 - Multimedia Computing Gerald Friedland and Ramesh Jain Index More information Index Aboud, G.D., 302 machine learning algorithms, 235–36 Absolute threshold of hearing (ATH), 165–66 machine vision algorithms, 235 Accelerometers, 30–31 MP3 format, 168–70 Acoustic event detection (AED), 235 , 281–82 MPEG-1 algorithm, 151–53 Acoustic Long-Term Average Spectrum (LTAS), 239 Sequential Minimal Optimization (SMO) Acoustic pitch, 238 algorithm, 257 Actuators, 28–29 Viterbi algorithm, 259 , 272–73 AdaBoost learning algorithm, 281 x-means algorithm, 143–44 Adaptive codebook, 184 Allen, J.F., 34 Addison, Paul S., 172 American National Standards Institute (ANSI), 53 ADPCM algorithm, 148–50 Anderson, C.A., 121 ADRS envelope, 47 ANNs (Artifi cial Neural Networks), 252–55 Advanced perceptual compression Anstis, S.M., 302 convolution theorem, 160–61 Apple, 108 Cooley-Tukey algorithm, 157 Apple iTunes, 202 Discrete Cosine Transform (DCT), 158–60 Application layer QoS control, 100 Discrete Fourier Transform (DFT), 156–58 Approximants, 44 Fast Cosine Transform, 157 Arithmetic coding, 135–38 , 136t.11.5 Fast Fourier Transform, 157 Artifi cial intelligence, 236 JPEG format and, 161–64 Artifi cial Neural Networks (ANNs), 252–55 MP3 format and, 168–70 ATH (Absolute threshold of hearing), 165–66 overview, 156 Attack, 47 perceptual video compression, 170–71 . Audio editing, 73–74 ( see also Perceptual video compression) Audio processing, 235 psychoacoustics and, 164–68 Audio sensors, 29 AED (Acoustic event detection), 235 , 281–82 Audiovisual media, 7–8 Ahmed, N., 171–72 Autostereoptic displays, 59 A-law, 145–46 A-weighting scheme, 37 Algorithms AdaBoost learning algorithm, 281 Baars, B.J., 93 ADPCM algorithm, 148–50 Background, signal processing and, 223–24 arithmetic coding, 135–38 , 136t.11.5 Bark scale, 165 , 217 Cooley-Tukey algorithm, 157 Barkhausen, Heinrich, 165 H u ff man coding, 128–31 Barrow, H.G., 302 k-means algorithm, 142–43 Bayesian Information Criterion (BIC), 252 , 262 , Lempel-Ziv algorithms, 131–35 279–80 LZ77 algorithm, 131–33 , 132t.11.3 Belongie, S., 302 LZ78 algorithm, 133–36 , 133t.11.4 Berkeley, George, 290 , 302 LZC algorithm, 135 Bickford, A.C., 49 LZW algorithm, 135 Big Data, 11 317 © in this web service Cambridge University Press www.cambridge.org Cambridge University Press 978-0-521-76451-3 - Multimedia Computing Gerald Friedland and Ramesh Jain Index More information 318 Index Binary Format for Scenes (BIFS), 171 recall, 194 Bing, 297 relevance feedback, 194 , 196–98 Blackstock, D.T., 48 term frequency/inverse document frequency Blu-ray format (TF/IDF), 195–96 compression and, 124 Web Image Search, 198 diff erential coding and, 151 Cloud computing, 11 , 308 lossy compression and, 141 CMYK color space, 61 Body transfer illusion, 84 Codd, Edgar, 189–90 Bohr, Niels, 305 Code Excited Linear Prediction (CELP) Books, 67–68 adaptive codebook, 184 Botvinick, M., 84 bitstream, 185t.14.1 Bovik, Al, 231 fi xed codebook, 184–85 Brandenburg, K., 172 line spectral frequencies (LSF), 183–84 Broughton, S.A., 171–72 line spectral pairs (LSP), 183–84 Bryan, K., 171–72 overview, 182–83 Bunke, Horst, 287 Cohen, J.D., 84 Bureau, M., 121 Color histograms, 243–44 Bushman, B.J., 121 Color spaces, 61–64 CIELAB color space, 62–64 Caitlin, D.E., 25 CIEXYZ color space, 63–64 Calvert, Gemma, 93 CMYK color space, 61 Cameras, 54–57 RGB color space, 61 autostereoptic displays, 59 YUV color space, 62 digital cameras, 57 Color vision, 60–61 Exif format and, 24–25 , 161 , 298–99 Communication image formation, 54–56 defi ned, 16 line of sight, 56 evolution of technology, 8–10 , 8t.2.1 moving camera, moving objects (MCMO), 282–83 in human society, 7–8 moving camera, stationary objects (MCSO), 282–83 overview, 25 parallax barrier method, 59 Compasses, 30–31 resolution, 56 Compression stationary camera, moving objects (SCMO), 282–83 advanced perceptual compression. (see Advanced stationary camera, stationary objects (SCSO), perceptual compression) 282–83 algorithms, 128–38 television and, 56–57 arithmetic coding, 135–38 , 136t.11.5 3D video, 57 , 59 DEFLATE method, 133 video cameras, 56–57 dynamic range compression (DRC), 216–17 Candelas, 52 entropy and, 127 Carryer, J. Edward, 34 H u ff man coding, 128–31 CBIR. See Content based image retrieval (CBIR) information content and, 125–32 , 126t.11.1. , C E L P. See Code Excited Linear Prediction (CELP) 127t.11.2 Challenges in multimedia systems Lempel-Ziv algorithms, 131–35 context versus content, 23–25 Lossy compression. (see Lossy semantic gap, 22–23 , 25 compression) overview, 21 LZ77 algorithm, 131–33 , 132t.11.3 Chroma quantization, 162 LZ78 algorithm, 133–36 , 133t.11.4 CIELAB color space, 62–64 LZC algorithm, 135 CIEXYZ color space, 63–64 LZW algorithm, 135 Clark, A.B., 146 overview, 4 , 124 Classic information retrieval perceptual video compression, 170–71 . logical representation, 193–94 ( see also Perceptual video compression) Multimedia Information Retrieval (MIR) run-length encoding (RLE), 124–25 compared, 192–98 source coding theorem, 127 overview, 192–94 speech compression. ( see Speech compression) PageRank, 194–95 weakness of entropy-based compression precision, 194 methods, 138 ranking, 194 Computer graphics, 235 © in this web service Cambridge University Press www.cambridge.org Cambridge University Press 978-0-521-76451-3 - Multimedia Computing Gerald Friedland and Ramesh Jain Index More information Index 319 Computing audiovisual media and, 7–8 evolution of technology, 8–10 , 9t.2.2 communication in human society, 7–8 expected results of computing, 11–12 elements of multimedia computing. (see Elements personal computers, 10–11 of multimedia computing) Connected components, signal processing and, 223 emerging applications and, 11 Connectivity, signal processing and, 223 evolution of computing and communication Content analysis. See Multimedia content analysis technology, 8–10 , 8t.2.1. , 9t.2.2 Content analysis systems. See Multimedia content evolving nature of information and, 11–12 analysis systems expected results of computing and, 11–12 Content based image retrieval (CBIR), 199–205 experiential environments and, 12–13 , 14 overview, 199–201 formal defi nition, 13 Query by Humming (QbH), 202 multimedia aspect, 10–13 semantic content-based retrieval, 202–3 personal computers and, 10–11 tag-based retrieval, 203–4 printing press and, 7 , 13 video retrieval, 204–5 storage and recording technology and, 8 Content replication, 101 telegraphy and, 7 Content segments, 69 telephony and, 10 Context versus content DEFLATE method, 133 connecting data and users, 292 DER (Diarization Error Rate), 280 context defi ned, 293–94 Desktop metaphor, 111 , 112 context in content, 294 , 295–96 Detection error tradeoff (DET) curve, 264 context only image search, 297 Device parameters, 297–99 data acquisition context, 294–95 derived metal layer, 298 device parameters, 297–99 . ( see also Device human induced metal layer, 298 parameters) meta layer, 297 interpretation context, 295 optical meta layer, 297 overview, 23–25 , 290–91 overview, 294 perceivers, 299–302 . ( see also Perceivers) pixel/spectral layer, 297 smart phone photo management, 300–2 spatial metal layer, 298 types of context, 294–95 temporal metal layer, 297 v e r i fi cation vision, 296–97 DFT (Discrete Fourier Transform), 156–58 Continuous media distribution services, 100–1 Dialog boxes, 110 Contours, 227 Diarization Error Rate (DER), 280 Control theory, 25 Dictionaries, 16–17 Conventional video encoding, 170–71 Diff erential coding, 147–53 Convolution theorem, 160–61 ADPCM algorithm, 148–50 Cooley-Tukey algorithm, 157 in audio, 148–50 Countermeasures regarding privacy, 118 in images, 150–51 Crocker, Lee Daniel, 151 MPEG-1 algorithm, 151–53 CRT technology, 58 Paeth fi lter, 150–51 PNG format, 150–51 , 151t.12.1 Data acquisition and organization in in video, 151–53 documents, 71–72 Digital cameras Data acquisition context, 294–95 Exif format and, 24–25 , 161 , 298–99 Databases overview, 57 logical level of data, 190 Digital television Multimedia Information Retrieval (MIR) and, diff erential coding and, 151 189–91 lossy compression and, 141 organization, storage, management, and retrieval Digital video discs (DVDs) (OSMR), 189–90 diff erential coding and, 151 physical level of data, 190 lossy compression and, 141 view level of data, 190–91 Digitization, 32–33 Daubechies, Ingrid, 172 discretization error, 33 DCT (Discrete Cosine Transform), 158–60 Nyquist frequency, 33 Decay, 47 quantization, 32 Decibel scale, 37 quantization noise, 33 D e fi ning multimedia systems sampling, 32 © in this web service Cambridge University Press www.cambridge.org Cambridge University Press 978-0-521-76451-3 - Multimedia Computing Gerald Friedland and Ramesh Jain Index More information 320 Index Diphthongs, 44 Edges, 226–28 Discrete Cosine Transform (DCT), 158–60 contours, 227 Discrete Fourier Transform (DFT), 156–58 detection of, 228 Discretization error, 33 edge detectors, 227 Distraction, 120 edge fragments, 227 Dix, Alan, 114 , 121 edge linking, 227 Documents edge orientations, 227 audio editing, 73–74 edge points, 227 audiovisual technology and, 68–69 enhancement of, 227 books, 67–68 fi ltering of, 227 content segments, 69 isotropic operators, 227 current authoring environments, 78–79 line discontinuities, 226 data acquisition and organization, 71–72 ramp edges, 226 defi ned, 67–69 roof edges, 226 dynamic documents, 71 step discontinuities, 226 editing, 72 vectors, 227 elements of multimedia authoring Edison, Th omas
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  • Audio Processing and Loudness Estimation Algorithms with Ios Simulations

    Audio Processing and Loudness Estimation Algorithms with Ios Simulations

    Audio Processing and Loudness Estimation Algorithms with iOS Simulations by Girish Kalyanasundaram A Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science Approved September 2013 by the Graduate Supervisory Committee: Andreas Spanias, Chair Cihan Tepedelenlioglu Visar Berisha ARIZONA STATE UNIVERSITY December 2013 ABSTRACT The processing power and storage capacity of portable devices have improved considerably over the past decade. This has motivated the implementation of sophisticated audio and other signal processing algorithms on such mobile devices. Of particular interest in this thesis is audio/speech processing based on perceptual criteria. Specifically, estimation of parameters from human auditory models, such as auditory patterns and loudness, involves computationally intensive operations which can strain device resources. Hence, strategies for implementing computationally efficient human auditory models for loudness estimation have been studied in this thesis. Existing algorithms for reducing computations in auditory pattern and loudness estimation have been examined and improved algorithms have been proposed to overcome limitations of these methods. In addition, real-time applications such as perceptual loudness estimation and loudness equalization using auditory models have also been implemented. A software implementation of loudness estimation on iOS devices is also reported in this thesis. In addition to the loudness estimation algorithms and software, in this thesis project we also created new illustrations of speech and audio processing concepts for research and education. As a result, a new suite of speech/audio DSP functions was developed and integrated as part of the award-winning educational iOS App 'iJDSP.” These functions are described in detail in this thesis. Several enhancements in the architecture of the application have also been introduced for providing the supporting framework for speech/audio processing.