International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Soft-decision correlation decoding with retransmission requests for block codes is proposed and the resulting performance is analyzed. The correlation decoding rule is modified to allow retransmission requests when the received word is rendered unreliable by the channel noise. The modification is realized by a reduction in the volume in Euclidean space of the decoding region corresponding to each codeword. The performance analysis reveals the typical throughput - reliability trade-off characteristic of error control systems which employ retransmissions. Performance comparisons with hard-decision decoding reveal performance improvements beyond those attainable with hard-decision decoding algorithms. The proposed soft-decision decoding rule permits the use of a simplified codeword searching algorithm which reduces the complexity of the correlation decoder to the point where practical implementation is feasible.

Zugl.: Zürich, Techn. Hochsch., Diss., 2009

[pt] A tecnologia de rádios cognitivos está surgindo como uma eficaz alternativa para aumentar o número de usuários de serviços de dados em banda larga em diversas faixas de frequências. O conceito pretende resolver a escassez do espectro a partir de duas características básicas: a capacidade cognitiva e a capacidade de reconfiguração. A capacidade cognitiva refere-se à habilidade do rádio de conhecer o meio no qual pretende operar, identificar de forma oportunista os espaços espectrais não utilizados na sua totalidade ou em alguns intervalos de tempo e, a partir da sua reconfiguração transmitir informação sem provocar interferência no usuário principal da porção do espectro utilizado. Esta dissertação de mestrado apresenta os resultados obtidos da análise do desempenho de regras de fusão para sensoriamento remoto cooperativo. Na primeira parte são abordados os fundamentos teóricos de Rádio cognitivo e os aspectos mais importantes do gerenciamento do espectro radioelétrico, além dos fundamentos do detector de energia, detector mais simples de implementar e utilizar para o sensoriamento em redes cognitivas. A seguir são abordadas diferentes técnicas para sensoriamento em redes cognitivas, apresentando os esquemas de decisão e a detecção convencional do espectro cooperativo, os métodos Hard decision e Soft decision. Finalmente uma avaliação das regras de sensoriamento cooperativo em cenários reais é realizada para obter as conclusões do trabalho. / [en] Cognitive radio technology is emerging as an effective alternative to increase the number of users in several frequency bands. The concept aims to solve the scarcity of the spectrum from two basic characteristics: the cognitive capacity and the capacity of reconfiguration. Cognitive ability refers to the ability of the radio to know the medium in which it is operating, to identify in an opportunistic way the unused spectral spaces in their totality or in some intervals of time and, from its reconfiguration transmit information without causing interference in the user of the portion of the spectrum used. This dissertation presents the results obtained from the analysis of the performance of fusion rules for cooperative remote sensing. The first part deals with the theoretical foundations of cognitive radio and the most important aspects of radioelectric spectrum management, as well as the fundamentals of the energy detector, the most important detector used in cognitive networks. Next, different techniques for sensing in cognitive networks are presented, presenting decision schemes and conventional detection of the cooperative spectrum, the Hard decision and Soft decision methods. Finally, an evaluation of the rules of cooperative sensing in real scenarios is carried out to obtain the conclusions of the work.

In this thesis, we study the concept of scalable video coding as implemented in the extension to the H.264 video coding standard. Specifically, for the spatial and quality scalability scenarios, we propose an optimization algorithm based on the Soft Decision Quantization (SDQ) concept, which aims at jointly optimizing all layers being encoded. The performance of the algorithm was assessed in an SVC implementation. Experimental results show, that the proposed method significantly improves the coding efficiency when compared to an unmodified SVC encoder.

In this thesis, we study the concept of scalable video coding as implemented in the extension to the H.264 video coding standard. Specifically, for the spatial and quality scalability scenarios, we propose an optimization algorithm based on the Soft Decision Quantization (SDQ) concept, which aims at jointly optimizing all layers being encoded. The performance of the algorithm was assessed in an SVC implementation. Experimental results show, that the proposed method significantly improves the coding efficiency when compared to an unmodified SVC encoder.

Most of the conventional communication systems use channel interleaving as well as hard decision decoding in their designs, which lead to discarding channel memory and soft-decision information. This simplification is usually done since the complexity of handling the memory or soft-decision information is rather high. In this work, we design two lossy joint source-channel coding (JSCC) schemes that do not use explicit algebraic channel coding for a recently introduced channel model, in order to take advantage of both channel memory and soft-decision information. The channel model, called the non-binary noise discrete channel with queue based noise (NBNDC-QB), obtains closed form expressions for the channel transition distribution, correlation coefficient, and many other channel properties. The channel has binary input and $2^q$-ary output and the noise is a $2^q$-ary Markovian stationary ergodic process, based on a finite queue, where $q$ is the output's soft-decision resolution. We also numerically show that the NBNDC-QB model can effectively approximate correlated Rayleigh fading channels without losing its analytical tractability. The first JSCC scheme is the so called channel optimized vector quantizer (COVQ) and the second scheme consists of a scalar quantizer, a proper index assignment, and a sequence maximum a posteriori (MAP) decoder, designed to harness the redundancy left in the quantizer's indices, the channel's soft-decision output, and noise time correlation. We also find necessary and sufficient condition when the sequence MAP decoder is reduced to an instantaneous symbol-by-symbol decoder, i.e., a simple instantaneous mapping. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2011-09-25 19:43:28.785

To push the envelope of DCT-based lossy image/video compression, this thesis is motivated to revisit design of some fundamental blocks in image/video coding, ranging from source modelling, quantization table, quantizers, to entropy coding. Firstly, to better handle the heavy tail phenomenon commonly seen in DCT coefficients, a new model dubbed transparent composite model (TCM) is developed and justified. Given a sequence of DCT coefficients, the TCM first separates the tail from the main body of the sequence, and then uses a uniform distribution to model DCT coefficients in the heavy tail, while using a parametric distribution to model DCT coefficients in the main body. The separation boundary and other distribution parameters are estimated online via maximum likelihood (ML) estimation. Efficient online algorithms are proposed for parameter estimation and their convergence is also proved. When the parametric distribution is truncated Laplacian, the resulting TCM dubbed Laplacian TCM (LPTCM) not only achieves superior modeling accuracy with low estimation complexity, but also has a good capability of nonlinear data reduction by identifying and separating a DCT coefficient in the heavy tail (referred to as an outlier) from a DCT coefficient in the main body (referred to as an inlier). This in turn opens up opportunities for it to be used in DCT-based image compression. Secondly, quantization table design is revisited for image/video coding where soft decision quantization (SDQ) is considered. Unlike conventional approaches where quantization table design is bundled with a specific encoding method, we assume optimal SDQ encoding and design a quantization table for the purpose of reconstruction. Under this assumption, we model transform coefficients across different frequencies as independently distributed random sources and apply the Shannon lower bound to approximate the rate distortion function of each source. We then show that a quantization table can be optimized in a way that the resulting distortion complies with certain behavior, yielding the so-called optimal distortion profile scheme (OptD). Guided by this new theoretical result, we present an efficient statistical-model-based algorithm using the Laplacian model to design quantization tables for DCT-based image compression. When applied to standard JPEG encoding, it provides more than 1.5 dB performance gain (in PSNR), with almost no extra burden on complexity. Compared with the state-of-the-art JPEG quantization table optimizer, the proposed algorithm offers an average 0.5 dB gain with computational complexity reduced by a factor of more than 2000 when SDQ is off, and a 0.1 dB performance gain or more with 85% of the complexity reduced when SDQ is on. Thirdly, based on the LPTCM and OptD, we further propose an efficient non-predictive DCT-based image compression system, where the quantizers and entropy coding are completely re-designed, and the relative SDQ algorithm is also developed. The proposed system achieves overall coding results that are among the best and similar to those of H.264 or HEVC intra (predictive) coding, in terms of rate vs visual quality. On the other hand, in terms of rate vs objective quality, it significantly outperforms baseline JPEG by more than 4.3 dB on average, with a moderate increase on complexity, and ECEB, the state-of-the-art non-predictive image coding, by 0.75 dB when SDQ is off, with the same level of computational complexity, and by 1 dB when SDQ is on, at the cost of extra complexity. In comparison with H.264 intra coding, our system provides an overall 0.4 dB gain or so, with dramatically reduced computational complexity. It offers comparable or even better coding performance than HEVC intra coding in the high-rate region or for complicated images, but with only less than 5% of the encoding complexity of the latter. In addition, our proposed DCT-based image compression system also offers a multiresolution capability, which, together with its comparatively high coding efficiency and low complexity, makes it a good alternative for real-time image processing applications.

Ulm, Univ., Diss., 2008.

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