The design of finite-state machines for quantization using simulated annealing

Ankara : Department of Electrical and Electronics Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1993.Thesis (Master's) -- Bilkent University, 1993.Includes bibliographical references leaves 121-125In this thesis, the combinatorial optimization algorithm known as simulated annealing (SA) is applied to the solution of the next-state map design problem of data compression systems based on finite-state machine decoders. These data compression systems which include finite-state vector ciuantization (FSVQ), trellis waveform coding (TWC), predictive trellis waveform coding (PTWC), and trellis coded quantization (TCQ) are studied in depth. Incorporating generalized Lloyd algorithm for the optimization of output map to SA, a finite-state machine decoder design algorithm for the joint optimization of output map and next-state map is constructed. Simulation results on several discrete-time sources for FSVQ, TWC and PTWC show that decoders with higher performance are obtained by the SA-I-CLA algorithm, when compared to other related work in the literature. In TCQ, simulation results are obtained for sources with memory and new observations are made.Kuruoğlu, Ercan EnginM.S

The role of meta-cognitions and thought control techniques in predisposition to auditory and visual hallucinations

Objectives. This study examines the relationship between a predisposition to hallucinations and meta-cognitive variables and thought-control techniques, controlling for the possible effect of anxiety. In order to do so, we start out with the hypothesis that anxiety does not, in itself, explain the association between meta-cognitions and a predisposition to auditory and visual hallucinations. Design. A within-participants correlational design was employed. Methods. Four psychometric tests relating to predisposition to hallucinations, anxiety, meta-cognitions and thought-control techniques were administered to 150 participants. Results. It was found that, after controlling for participants' anxiety levels, the ‘loss of cognitive confidence’ factor predicted the score on the scale of predisposition to both auditory and visual hallucinations. Thought-control strategies based on worry were also found to be predictive of a greater predisposition to hallucinations, regardless of whether or not participants' anxiety level was controlled. Conclusions. Meta-cognitive variables of cognitive confidence and thought control through worry are positively associated with a predisposition to hallucinations. Limitations. The correlational nature of the design does not allow inferences about causal relationships

Relationship between worry and metacognitive beleifs from the viewpoint of cognitive behavioral theory

制度:新 ; 報告番号:甲2970号 ; 学位の種類:博士(人間科学) ; 授与年月日:2010/1/20 ; 早大学位記番号:新522

High-performance compression of visual information - A tutorial review - Part I : Still Pictures

Digital images have become an important source of information in the modern world of communication systems. In their raw form, digital images require a tremendous amount of memory. Many research efforts have been devoted to the problem of image compression in the last two decades. Two different compression categories must be distinguished: lossless and lossy. Lossless compression is achieved if no distortion is introduced in the coded image. Applications requiring this type of compression include medical imaging and satellite photography. For applications such as video telephony or multimedia applications, some loss of information is usually tolerated in exchange for a high compression ratio. In this two-part paper, the major building blocks of image coding schemes are overviewed. Part I covers still image coding, and Part II covers motion picture sequences. In this first part, still image coding schemes have been classified into predictive, block transform, and multiresolution approaches. Predictive methods are suited to lossless and low-compression applications. Transform-based coding schemes achieve higher compression ratios for lossy compression but suffer from blocking artifacts at high-compression ratios. Multiresolution approaches are suited for lossy as well for lossless compression. At lossy high-compression ratios, the typical artifact visible in the reconstructed images is the ringing effect. New applications in a multimedia environment drove the need for new functionalities of the image coding schemes. For that purpose, second-generation coding techniques segment the image into semantically meaningful parts. Therefore, parts of these methods have been adapted to work for arbitrarily shaped regions. In order to add another functionality, such as progressive transmission of the information, specific quantization algorithms must be defined. A final step in the compression scheme is achieved by the codeword assignment. Finally, coding results are presented which compare stateof- the-art techniques for lossy and lossless compression. The different artifacts of each technique are highlighted and discussed. Also, the possibility of progressive transmission is illustrated

Trellis-coded quantization with unequal distortion.

Kwong Cheuk Fai.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 72-74).Abstracts in English and Chinese.Acknowledgements --- p.iAbstract --- p.iiTable of Contents --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Quantization --- p.2Chapter 1.2 --- Trellis-Coded Quantization --- p.3Chapter 1.3 --- Thesis Organization --- p.4Chapter 2 --- Trellis-Coded Modulation --- p.6Chapter 2.1 --- Convolutional Codes --- p.7Chapter 2.1.1 --- Generator Polynomials and Generator Matrix --- p.9Chapter 2.1.2 --- Circuit Diagram --- p.10Chapter 2.1.3 --- State Transition Diagram --- p.11Chapter 2.1.4 --- Trellis Diagram --- p.12Chapter 2.2 --- Trellis-Coded Modulation --- p.13Chapter 2.2.1 --- Uncoded Transmission verses TCM --- p.14Chapter 2.2.2 --- Trellis Representation --- p.17Chapter 2.2.3 --- Ungerboeck Codes --- p.18Chapter 2.2.4 --- Set Partitioning --- p.19Chapter 2.2.5 --- Decoding for TCM --- p.22Chapter 3 --- Trellis-Coded Quantization --- p.26Chapter 3.1 --- Scalar Trellis-Coded Quantization --- p.26Chapter 3.2 --- Trellis-Coded Vector Quantization --- p.31Chapter 3.2.1 --- Set Partitioning in TCVQ --- p.33Chapter 3.2.2 --- Codebook Optimization --- p.34Chapter 3.2.3 --- Numerical Data and Discussions --- p.35Chapter 4 --- Trellis-Coded Quantization with Unequal Distortion --- p.38Chapter 4.1 --- Design Procedures --- p.40Chapter 4.2 --- Fine and Coarse Codebooks --- p.41Chapter 4.3 --- Set Partitioning --- p.44Chapter 4.4 --- Codebook Optimization --- p.45Chapter 4.5 --- Decoding for Unequal Distortion TCVQ --- p.46Chapter 5 --- Unequal Distortion TCVQ on Memoryless Gaussian Source --- p.47Chapter 5.1 --- Memoryless Gaussian Source --- p.49Chapter 5.2 --- Set Partitioning of Codewords of Memoryless Gaussian Source --- p.49Chapter 5.3 --- Numerical Results and Discussions --- p.51Chapter 6 --- Unequal Distortion TCVQ on Markov Gaussian Source --- p.57Chapter 6.1 --- Markov Gaussian Source --- p.57Chapter 6.2 --- Set Partitioning of Codewords of Markov Gaussian Source --- p.58Chapter 6.3 --- Numerical Results and Discussions --- p.59Chapter 7 --- Conclusions --- p.70Bibliography --- p.7

DCT-based Image/Video Compression: New Design Perspectives

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

Optimal soft-decoding combined trellis-coded quantization/modulation.

Chei Kwok-hung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 66-73).Abstracts in English and Chinese.Chapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Typical Digital Communication Systems --- p.2Chapter 1.1.1 --- Source coding --- p.3Chapter 1.1.2 --- Channel coding --- p.5Chapter 1.2 --- Joint Source-Channel Coding System --- p.5Chapter 1.3 --- Thesis Organization --- p.7Chapter Chapter 2 --- Trellis Coding --- p.9Chapter 2.1 --- Convolutional Codes --- p.9Chapter 2.2 --- Trellis-Coded Modulation --- p.12Chapter 2.2.1 --- Set Partitioning --- p.13Chapter 2.3 --- Trellis-Coded Quantization --- p.14Chapter 2.4 --- Joint TCQ/TCM System --- p.17Chapter 2.4.1 --- The Combined Receiver --- p.17Chapter 2.4.2 --- Viterbi Decoding --- p.19Chapter 2.4.3 --- Sequence MAP Decoding --- p.20Chapter 2.4.4 --- Sliding Window Decoding --- p.21Chapter 2.4.5 --- Block-Based Decoding --- p.23Chapter Chapter 3 --- Soft Decoding Joint TCQ/TCM over AWGN Channel --- p.25Chapter 3.1 --- System Model --- p.26Chapter 3.2 --- TCQ with Optimal Soft-Decoder --- p.27Chapter 3.3 --- Gaussian Memoryless Source --- p.30Chapter 3.3.1 --- Theorem Limit --- p.31Chapter 3.3.2 --- Performance on PAM Constellations --- p.32Chapter 3.3.3 --- Performance on PSK Constellations --- p.36Chapter 3.4 --- Uniform Memoryless Source --- p.38Chapter 3.4.1 --- Theorem Limit --- p.38Chapter 3.4.2 --- Performance on PAM Constellations --- p.39Chapter 3.4.3 --- Performance on PSK Constellations --- p.40Chapter Chapter 4 --- Soft Decoding Joint TCQ/TCM System over Rayleigh Fading Channel --- p.42Chapter 4.1 --- Wireless Channel --- p.43Chapter 4.2 --- Rayleigh Fading Channel --- p.44Chapter 4.3 --- Idea Interleaving --- p.45Chapter 4.4 --- Receiver Structure --- p.46Chapter 4.5 --- Numerical Results --- p.47Chapter 4.5.1 --- Performance on 4-PAM Constellations --- p.48Chapter 4.5.2 --- Performance on 8-PAM Constellations --- p.50Chapter 4.5.3 --- Performance on 16-PAM Constellations --- p.52Chapter Chapter 5 --- Joint TCVQ/TCM System --- p.54Chapter 5.1 --- Trellis-Coded Vector Quantization --- p.55Chapter 5.1.1 --- Set Partitioning in TCVQ --- p.56Chapter 5.2 --- Joint TCVQ/TCM --- p.59Chapter 5.2.1 --- Set Partitioning and Index Assignments --- p.60Chapter 5.2.2 --- Gaussian-Markov Sources --- p.61Chapter 5.3 --- Simulation Results and Discussion --- p.62Chapter Chapter 6 --- Conclusion and Future Work --- p.64Chapter 6.1 --- Conclusion --- p.64Chapter 6.2 --- Future Works --- p.65Bibliography --- p.66Appendix-Publications --- p.7