We propose two generalized block-diagonalization (BD) schemes for multiple-input multiple-output (MIMO) relay broadcasting systems with no channel state information (CSI) at base station. We first introduce a generalized zero forcing (ZF) scheme that reduces the complexity of the traditional BD scheme. Then the optimal power loading matrix for the proposed scheme is analyzed and the closed-form solution is derived. Furthermore, an enhanced scheme is proposed by employing the minimum-mean-squared-error (MMSE) criterion. Simulation results show that the proposed generalized MMSE scheme outperforms the other schemes and the optimal power loading scheme improves the sum-rate performance efficiently.

Xianan Wang

Xiaoxiang Wang

Wenrong Gong

Zijia Huang

Generalized Block-Diagonalization Schemes for MIMO Relay Broadcasting Systems

The research presented in this paper improves the structure-criterion switching performance of the blind decision feedback equalizer (DFE) which eliminates error propagation effects by optimizing both the structure and the cost criterion. To conquer the complexity of the 64-QAM (quadrature amplitude modulated) signal constellation, the stochastic entropy-gradient algorithm is additionally regularized by the coefficient leaky term to avoid a coefficients norm overgrowth of the received signal whitener. Effectively, the leak of coefficients is employed to ensure a stable structure-criterion switching of DFE between blind and decision-directed operation modes. The optimization of the resulting whitening algorithm is achieved by means of two free, leaky and entropic, parameters which act in opposition to each other. Both, the influence of the 64-QAM signal on the feedback filter behavior and the parametric optimization of the whitening algorithm are analyzed through simulations.

Vladimir R. Krsti&#x107;

Miroslav L. Duki&#x107;

Decision Feedback Blind Equalizer with Tap-Leaky Whitening for Stable Structure-Criterion Switching

We propose a new watermarking method based on quantization index modulation. A concept of initial data loss is introduced in order to increase capacity of the watermarking channel under high intensity additive white Gaussian noise. According to the concept some samples in predefined positions are ignored even though this produces errors in the initial stage of watermark embedding. The proposed method also exploits a new form of distribution of quantized samples where samples that interpret &#x201C;0&#x201D; and &#x201C;1&#x201D; have differently shaped probability density functions. Compared to well-known watermarking schemes, this provides an increase of capacity under noise attack and introduces a distinctive feature. Two criteria are proposed that express the feature numerically. The criteria are utilized by a procedure for estimation of a gain factor after possible gain attack. Several state-of-the-art quantization-based watermarking methods were used for comparison on a set of natural grayscale images. The superiority of the proposed method has been confirmed for different types of popular attacks.

Yevhen Zolotavkin

Martti Juhola

A New QIM-Based Watermarking Method Robust to Gain Attack

A new representation of the Fourier transform in terms of time and scale localization is discussed that uses a newly coined <i>A</i>-wavelet transform (Grigoryan 2005). The <i>A</i>-wavelet transform uses cosine- and sine-wavelet type functions, which employ, respectively, cosine and sine signals of length <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.1625805pt" id="M1" height="8.76568pt" version="1.1" viewBox="0 -8.6031 14.5101 8.76568" width="14.5101pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path id="g113-51" d="M412 140C382 77 369 73 315 73H129L270 222C362 320 402 379 402 466C402 571 322 635 234 635C177 635 130 609 99 576L42 495L64 475C90 514 133 568 201 568C274 568 318 519 318 435C318 349 255 267 193 193C144 135 87 78 32 23V0H405C417 45 427 89 440 131L412 140Z"></path></g><g transform="matrix(.0135,0,0,-0.0135,6.503,0)"><path id="g113-238" d="M574 449L545 460C526 432 516 430 487 430C404 430 311 435 226 435C104 435 56 379 25 341L43 318C81 354 121 372 181 372C161 246 87 53 23 3L30 -12C48 -12 88 -4 113 11C157 75 207 248 232 371L386 367L326 109C321 86 318 66 318 50C318 4 339 -12 366 -12C410 -12 461 21 505 69L492 96C467 79 434 60 418 60C406 60 400 78 411 147C422 217 439 300 457 366C487 366 524 367 536 370C547 385 558 408 574 449Z"></path></g></svg>. For a given frequency <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.16258pt" id="M2" height="6.31346pt" version="1.1" viewBox="0 -6.15088 8.64375 6.31346" width="8.64375pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path id="g113-246" d="M615 290C615 392 563 448 529 448C511 448 491 441 484 434L483 425C518 400 546 349 546 273C546 158 504 46 417 46C381 46 356 77 356 142C356 184 380 293 392 334L387 340L307 323C305 288 296 228 287 188C265 94 221 52 180 52C144 52 112 89 112 171C112 278 164 372 241 430L226 454C106 394 23 288 23 155C23 40 79 -12 139 -12C184 -12 245 32 285 85C292 31 322 -12 376 -12C423 -12 470 18 501 45C570 104 615 184 615 290Z"></path></g></svg>, the cosine- and sine-wavelet type functions are evaluated at time points separated by <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-2.208321pt" id="M3" height="11.7327pt" version="1.1" viewBox="0 -9.52438 28.7423 11.7327" width="28.7423pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><use xlink:href="#g113-51"></use></g><g transform="matrix(.0135,0,0,-0.0135,6.503,0)"><use xlink:href="#g113-238"></use></g><g transform="matrix(.0135,0,0,-0.0135,14.511,0)"><path id="g113-48" d="M368 703H309L44 -163H104L368 703Z"></path></g><g transform="matrix(.0135,0,0,-0.0135,20.098,0)"><use xlink:href="#g113-246"></use></g></svg> on the time-axis. This is a two-parameter representation of a signal in terms of time and scale (frequency), and can find out frequency contents present in the signal at any time point using less computation. In this paper, we extend this work to provide further signal information in a better way and name it as <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" id="M4" height="10.4409pt" version="1.1" viewBox="0 -10.4409 15.3146 10.4409" width="15.3146pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path id="g113-66" d="M686 28C612 35 607 44 591 112C563 234 541 360 519 489L489 666L457 658L147 121C100 40 89 36 24 28L17 0H240L250 28C168 34 159 41 190 101L262 237H482C495 180 503 137 510 91C517 47 514 35 441 28L433 0H677L686 28ZM475 280H285L429 541H431L475 280Z"></path></g><g transform="matrix(.0095,0,0,-0.0095,9.52,-5.984)"><path id="g50-43" d="M486 158C486 177 478 202 466 220C413 228 386 236 336 262C386 288 413 297 466 304C478 323 486 347 485 366C470 376 444 381 422 380C389 338 368 319 321 288C323 345 329 372 349 422C339 442 322 461 305 470C289 461 271 442 262 422C281 372 287 345 290 288C243 319 222 338 189 380C167 381 142 376 125 366C125 347 133 322 145 304C198 296 225 288 275 262C225 236 198 227 145 220C133 201 125 177 126 158C141 148 167 143 189 144C222 186 243 205 290 236C288 179 282 152 262 102C272 82 289 63 306 54C322 63 340 82 350 102C330 152 324 179 321 236C368 205 390 186 422 144C444 143 470 148 486 158Z"></path></g></svg>-wavelet transform. In our proposed work, we use cosine and sine signals defined over the time intervals, each of length <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-2.3032pt" id="M5" height="12.5639pt" version="1.1" viewBox="0 -10.2607 60.6838 12.5639" width="60.6838pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><use xlink:href="#g113-51"></use></g><g transform="matrix(.0135,0,0,-0.0135,6.503,0)"><use xlink:href="#g113-238"></use></g><g transform="matrix(.0135,0,0,-0.0135,14.511,0)"><path id="g113-110" d="M766 88L752 113C719 83 690 64 681 64C674 64 672 74 679 103L724 292C758 436 724 448 701 448C680 448 666 442 639 429C594 407 514 350 441 252H439L447 289C476 423 450 448 419 448C398 448 379 441 355 427C307 400 234 344 162 249H160L180 324C203 409 197 448 170 448C144 448 82 413 23 349L35 321C57 343 96 374 108 374C115 374 117 371 111 341C87 227 57 112 24 -6L32 -12C53 -4 81 4 108 6C119 68 134 128 149 171C177 229 309 383 364 383C387 383 388 355 373 282C354 190 330 92 303 -6L309 -12C332 -4 356 3 386 6C396 63 411 122 424 171C458 236 590 383 642 383C658 383 664 369 652 315L603 91C587 20 593 -12 619 -12C642 -12 708 23 766 88Z"></path></g><g transform="matrix(.0135,0,0,-0.0135,25.201,0)"><use xlink:href="#g113-48"></use></g><g transform="matrix(.0135,0,0,-0.0135,30.788,0)"><path id="g113-41" d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"></path></g><g transform="matrix(.0135,0,0,-0.0135,35.476,0)"><use xlink:href="#g113-51"></use></g><g transform="matrix(.0095,0,0,-0.0095,41.979,-5.984)"><path id="g50-111" d="M504 89L488 119C453 85 428 69 416 69C408 69 405 75 412 102L462 304C492 438 460 451 436 451S388 441 356 423C311 397 229 336 170 256H168L189 340C208 418 200 451 177 451C144 451 82 413 24 352L40 322C65 345 98 369 108 369C114 369 119 363 112 335L28 -3L36 -12C54 -3 83 4 112 10C125 73 138 122 153 174C205 258 328 382 376 382C395 382 399 369 384 305L330 93C312 25 323 -12 351 -12C378 -12 439 21 504 89Z"></path></g><g transform="matrix(.0135,0,0,-0.0135,47.351,0)"><use xlink:href="#g113-246"></use></g><g transform="matrix(.0135,0,0,-0.0135,55.996,0)"><path id="g113-42" d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z"></path></g></svg>, <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.5418997pt" id="M6" height="10.8026pt" version="1.1" viewBox="0 -10.2607 37.5714 10.8026" width="37.5714pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><use xlink:href="#g113-110"></use></g><g transform="matrix(.0135,0,0,-0.0135,14.475,0)"><path id="g117-93" d="M531 71V127L115 310L531 494V550L57 335V285L531 71ZM531 -40V10H57V-40H531Z"></path></g><g transform="matrix(.0135,0,0,-0.0135,26.213,0)"><use xlink:href="#g113-51"></use></g><g transform="matrix(.0095,0,0,-0.0095,32.716,-5.984)"><use xlink:href="#g50-111"></use></g></svg>, <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.16258pt" id="M7" height="6.23217pt" version="1.1" viewBox="0 -6.06959 10.6895 6.23217" width="10.6895pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><use xlink:href="#g113-110"></use></g></svg> and <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.16258pt" id="M8" height="6.23217pt" version="1.1" viewBox="0 -6.06959 6.80119 6.23217" width="6.80119pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path id="g113-111" d="M495 86L479 114C446 82 419 66 409 66C401 66 401 72 406 97C420 166 436 231 453 297C489 435 454 448 428 448C406 448 384 439 354 422C305 394 222 327 161 247H159L183 345C200 415 194 448 173 448C143 448 82 410 23 351L38 325C64 349 95 371 105 371C111 371 116 365 109 336L25 -4L31 -12C50 -4 77 3 107 9C119 69 132 122 145 168C197 254 321 381 370 381C387 381 393 374 378 305L329 95C309 17 320 -12 345 -12C372 -12 430 19 495 86Z"></path></g></svg> are nonnegative integers, to develop cosine- and sine-type wavelets. Using smaller time intervals provides sharper frequency localization in the time-frequency plane as the frequency is inversely proportional to the time. It further reduces the computation for evaluating the Fourier transform at a given frequency. The <i>A</i>-wavelet transform can be derived as a special case of the <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" id="M9" height="10.4409pt" version="1.1" viewBox="0 -10.4409 15.3146 10.4409" width="15.3146pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><use xlink:href="#g113-66"></use></g><g transform="matrix(.0095,0,0,-0.0095,9.52,-5.984)"><use xlink:href="#g50-43"></use></g></svg>-wavelet transform.

Satish Chand

Efficient Time-Frequency Localization of a Signal

The need for adapting video stream delivery over heterogeneous and unreliable networks requires self-adaptive and error resilient coding. Network bandwidth fluctuations can be handled by means of a video coding scheme which adapts to the channel conditions. However, packet losses which are frequent in wireless networks can cause a mismatch during the reconstruction in the receiver end and result in an accumulation of errors which deteriorates the quality of the delivered video. A combination of multiple description coding in pixel domain and scalable video coding schemes which addresses both video adaptation and robustness to data loss is proposed in this paper. The proposed scheme combines error concealment with spatial video scalability. In order to improve the fidelity of the reconstructed to the original frames in presence of packet loss, a multilayer polyphase spatial decomposition algorithm is proposed. Classical multiple description methods interpolate the missing data which results in smoothing and artifact at object boundaries. The proposed algorithm addresses the quality degradation due to low-pass filtering effect of interpolation methods. We also comparatively analyze the trade-off between robustness to channel errors and coding efficiency.

Roya Choupani

Stephan Wong

Mehmet Tolun

Spatial Multiple Description Coding for Scalable Video Streams

This paper presents a model to predict video quality perceived by the broadcast digital television (DTV) viewer. We present how noise on DTV can introduce individual transport stream (TS) packet losses at the receiver. The type of these errors is different than the produced on IP networks. Different scenarios of TS packet loss are analyzed, including uniform and burst distributions. The results show that there is a high variability on the perceived quality for a given percentage of packet loss and type of error. This implies that there is practically no correlation between the type of error or the percentage of packets loss and the perceived degradation. A new metric is introduced, the<i> weighted percentage</i> of slice loss, which takes into account the affected slice type in each lost TS packet. We show that this metric is correlated with the video quality degradation. A novel parametric model for video quality estimation is proposed, designed, and verified based on the results of subjective tests in SD and HD. The results were compared to a standard model used in IP transmission scenarios. The proposed model improves Pearson Correlation and root mean square error between the subjective and the predicted MOS.

Jose Joskowicz

Rafael Sotelo

A Model for Video Quality Assessment Considering Packet Loss for Broadcast Digital Television Coded in H.264

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<p><em>International Journal of Digital Multimedia Broadcasting</em> provides a forum for engineers and researchers whose interests are in digital multimedia broadcasting to share recent developments and challenges in order to design new and improved systems.</p>


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International Journal of Digital Multimedia Broadcasting - Table of contents 2014

International Journal of Digital Multimedia Broadcasting | Hindawi

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International Journal of Digital Multimedia Broadcasting provides a forum for engineers and researchers whose interests are in digital multimedia broadcasting to share recent developments and challenges in order to design new and improved systems.

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International Journal of Digital Multimedia Broadcasting

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International Journal of Digital Multimedia Broadcasting