An adaptive quantization algorithm without side information for depth map coding

This paper presents a novel block-adaptive quantization scheme for efficient bit allocation without side information in depth map coding. Since the type of distortion in a depth map causes different effects in terms of the visual artifacts in a synthesized view, the proposed method adaptively assigns the number of bits according to the characteristics of the corresponding texture block. I have studied the details of the depth map and its rendered view distortion, modeled these analytically, and then proposed a new rate and distortion model for depth map coding. Finally, I derived a simple closed-form solution based on my proposed rate and distortion model, which determines the block-adaptive quantization parameter without any side information. Experimental results show that the proposed scheme can achieve coding gains of more than 0.6% and 1.4% for quarter- and full-resolution depth maps, respectively, in a multi-view-plus-depth 3D system.     

Adaptive space-time transmission with side information

In this letter, a simple adaptive transmit diversity technique with side information is presented. The proposed scheme has a number of attractive characteristics. First, it is simple to implement. Second, it is efficient in terms of bandwidth requirements for side information feedback. Finally, it adapts to the quality of the side information to accordingly configure the space-time signal set to be transmitted so that additional gain in signal-to-noise ratio can be achieved at the receiver. In effect, it can be viewed as a flexible combination of suboptimal beamforming and space-time block coding.     

Subband finite state scalar quantization

A new image-coding algorithm that exploits the relationship between various subbands of an image is presented in this correspondence. Using local variance for state selection and uniform threshold quantizers on subbands, we have achieved a reduction in quantization noise compared to other published techniques for Lena at various bit rates.     

SLM peak-power reduction without explicit side information

Selected mapping (SLM) peak-power reduction is distortionless as it selects the actual transmit signal from a set of alternative signals, which all represent the same information. The specific signal generation information needs to be transmitted and carefully protected against bit errors. Here, me propose an extension of SLM, which employs scrambling and refrains from the use of explicit side information in the receiver. Some additional complexity and nearly vanishing redundancy is introduced to achieve markedly improved transmit signal statistics. Even though SLM is applicable with any modulation, we concentrate on orthogonal frequency-division multiplexing (OFDM) in this letter     

Adaptive vector quantization

Annals of Telecommunications - In adaptive quantization, the parameters of a quantizer are updated during real-time operation based on observed information regarding the statistics of the signal...     

Asymptotic analysis of unslotted frequency-hop multiple-access without side information

In a packet radio network that employs frequency-hop transmission and error control coding, the use of side information permits identification and erasure of symbols that have been corrupted by multiple-access interference, and thus greatly enhances multiple-access capability. In one approach, side information is generated from an error detecting random code. We investigate the trade-off between the reliability of side information and the rate of the code, and derive the maximum allowable code rate to achieve a certain reliability of side information. An achievable region and the channel throughput are examined for an unslotted frequency-hop packet radio network that employs Reed-Solomon codes and side information generated within the radio by an error detecting code.     

Sequential scalar quantization of vectors: an analysis

Proposes an efficient vector quantization (VQ) technique called sequential scalar quantization (SSQ). The scalar components of the vector are individually quantized in a sequence, with the quantization of each component utilizing conditional information from the quantization of previous components. Unlike conventional independent scalar quantization (ISQ), SSQ has the ability to exploit intercomponent correlation. At the same time, since quantization is performed on scalar rather than vector variables, SSQ offers a significant computational advantage over conventional VQ techniques and is easily amenable to a hardware implementation. In order to analyze the performance of SSQ, the authors appeal to asymptotic quantization theory, where the codebook size is assumed to be large. Closed-form expressions are derived for the quantizer mean squared error (MSE). These expressions are used to compare the asymptotic performance of SSQ with other VQ techniques. The authors also demonstrate the use of asymptotic theory in designing SSQ for a practical application (color image quantization), where the codebook size is typically small. Theoretical and experimental results show that SSQ far outperforms ISQ with respect to MSE while offering a considerable reduction in computation over conventional VQ at the expense of a moderate increase in MSE.     

Trellis-based scalar vector quantization of sources with memory

The trellis-based scalar-vector quantizer (TB-SVQ) can achieve the rate-distortion performance bound for memoryless sources. This paper extends the scope of this quantizer to coding of sources with memory. First considered is a simple extension, called the predictive TB-SVQ, which applies a closed-loop predictive coding operation in each survivor path of the Viterbi codebook search algorithm. Although the predictive TB-SVQ outperforms all other known structured fixed-rate vector quantizers, due to practical reasons, it may not approach the rate-distortion limit. A new quantization scheme motivated by the precoding idea of Laroia et al. (1993), called the precoded TB-SVQ, is also considered; the granular gain is realized by the underlying trellis code while the combination of the precoder and the SVQ structure provides the boundary gain. This new quantization scheme is asymptotically optimal and can, in principle, approach the rate-distortion bound for Markov sources     

Efficient scalar quantization of exponential and Laplacian randomvariables

This paper presents solutions to the entropy-constrained scalar quantizer (ECSQ) design problem for two sources commonly encountered in image and speech compression applications: sources having the exponential and Laplacian probability density functions. We use the memoryless property of the exponential distribution to develop a new noniterative algorithm for obtaining the optimal quantizer design. We show how to obtain the optimal ECSQ either with or without an additional constraint on the number of levels in the quantizer. In contrast to prior methods, which require a multidimensional iterative solution of a large number of nonlinear equations, the new method needs only a single sequence of solutions to one-dimensional nonlinear equations (in some Laplacian cases, one additional two-dimensional solution is needed). As a result, the new method is orders of magnitude faster than prior ones. We show that as the constraint on the number of levels in the quantizer is relaxed, the optimal ECSQ becomes a uniform threshold quantizer (UTQ) for exponential, but not for Laplacian sources. We then further examine the performance of the UTQ and optimal ECSQ, and also investigate some interesting alternatives to the UTQ, including a uniform-reconstruction quantizer (URQ) and a constant dead-zone ratio quantizer (CDZRQ)     

Selected mapping without side information for PAPR reduction in OFDM

Selected mapping (SLM) is a technique used to reduce the peak-to-average power ratio (PAPR) in orthogonal frequency-division multiplexing (OFDM) systems. SLM requires the transmission of several side information bits for each data block, which results in some data rate loss. These bits must generally be channel-encoded because they are particularly critical to the error performance of the system. This increases the system complexity and transmission delay, and decreases the data rate even further. In this paper, we propose a novel SLM method for which no side information needs to be sent. By considering the example of several OFDM systems using either QPSK or 16- QAM modulation, we show that the proposed method performs very well both in terms of PAPR reduction and bit error rate at the receiver output provided that the number of subcarriers is large enough.     

SLM and PTS peak-power reduction of OFDM signals without side information

Selected mapping (SLM) and partial transmit sequence (PTS) are well-known techniques for peak-power reduction in orthogonal frequency-division multiplexing (OFDM). We derive a simplified maximum likelihood (ML) decoder for SLM and PTS that operates without side information. This decoder exploits the fact that the modulation symbols belong to a given constellation and that the multiple signals generated by the PTS or SLM processes are widely different in a Hamming distance sense. Pairwise error probability (PEP) analysis suggests how SLM and PTS vectors should be chosen. The decoder performs well over additive white Gaussian noise (AWGN) channels, fading channels, and amplifier nonlinearities.     

Adaptive vector quantization using a self-development neuralnetwork

A neural network model, called SPAN (space partition network), is presented. This model differs from most of the currently seen neural networks in that it allows a network to adapt its structure by adding neurons, killing neurons, and modifying the structural relationships between neurons in the network. An adaptive vector quantization source-coding system based on SPAN is proposed. The major advantage of using SPAN as the codebook of a vector quantizer is that SPAN can capture the local context of the source signal space and map onto a lattice structure. A fast codebook-searching method utilizing the local context of the lattice is proposed, and a coding scheme, called the path coding method, for eliminating the correlation buried in the source sequence is introduced. The performance of the proposed coder is compared to an LBG (Y. Linde, A. Buzo, and R.M. Gray, 1980) coder on synthesized Gauss-Markov sources. Simulation results show that, without using the path coding method, SPAN yields performance similar to an LBG coder; however, if the path coding method is used, SPAN displays a much better performance than the LBG for highly correlated signal sources     

Adaptive quantization for fading channels with feedback

The effect of the presence of a feedback channel on the transmission of information was first considered by Shannon, who showed that the capacity of a memoryless channel is not increased by the existence of a feedback link even if the feedback link is noiseless. Later it was shown that the information on a feedback channel can be used to improve considerably the performance of channel coding. In this work we study the transmission of an information source through a fading channel with feedback, modeled by a finite-state channel in the Gilbert-Elliot sense. We show that by employing the feedback information in the quantizer design for this finite-state channel, one can achieve lower overall distortion compared to the case where feedback is not available. The feedback channel is used to estimate the channel state using a hidden Markov model, and a quantizer matched to the channel state is chosen based on this information.     

Adaptive entropy-coded predictive vector quantization of images

The authors consider 2-D predictive vector quantization (PVQ) of images subject to an entropy constraint and demonstrate the substantial performance improvements over existing unconstrained approaches. They describe a simple adaptive buffer-instrumented implementation of this 2-D entropy-coded PVQ scheme which can accommodate the associated variable-length entropy coding while completely eliminating buffer overflow/underflow problems at the expense of only a slight degradation in performance. This scheme, called 2-D PVQ/AECQ (adaptive entropy-coded quantization), is shown to result in excellent rate-distortion performance and impressive quality reconstructions of real-world images. Indeed, the real-world coding results shown demonstrate little distortion at rates as low as 0.5 b/pixel     

Adaptive quantization of picture signals using spatial masking

Visual sensitivity of human observers decreases at and adjacent to large luminance changes: a fact well known to psychophysicists but not yet fully utilized for picture coding. In this paper we present a systematic investigation of these changes in visual sensitivity and apply it to adapt the quantizer of a predictive coder. The paper consists of three parts. In the first part, earlier psychophysical work in related areas relevant to picture coding is briefly reviewed. Certain simple measures of luminance activity (called the masking functions) are constructed. Subjective experiments which obtain fidelity measures related to the masking functions, using complex scenes (real-life pictures, head and shoulders view), are described. These relationships, called the visibility functions, express relationship between the relative amplitude accuracy required by the viewer and the masking functions. Perceptual, statistical, and contextual properties are inherent in them. Relationship between the visibility functions and some earlier measurements of psychovisual weighting functions are pointed out. In the second part, several adaptation strategies for the quantizer of a predictive DPCM coder are discussed. These include uniform as well as nonuniform quantizers with or without entropy constraints. These strategies are simulated on a computer, and the results are presented in the third part. The simulations indicate that, for the same picture quality, by using adaptive strategies, entropy reductions of about 30-50 percent are possible over nonadaptive techniques.     

Adaptive rate-distortion optimal in-loop quantization for matching pursuit

In this paper, an adaptive in-loop quantization technique is proposed for quantizing inner product coefficients in matching pursuit. For each matching pursuit (MP) stage a different quantizer is used based on the probability distribution of MP coefficients. The quantizers are optimized for a given rate budget constraint. Additionally, our proposed adaptive quantization scheme finds the optimal quantizers for each stage based on the already encoded inner product coefficients. Experimental results show that our proposed adaptive quantization scheme outperforms existing quantization methods used in matching pursuit image coding.     

Adaptive quantization: solution via nonadaptive linear control

A common nonlinear adaptive quantization problem is posed as a corresponding linear quadratic control problem. Adaptive quantizer algorithms are generated using methods of disturbance rejection in linear systems. Simulation with speech data shows that these algorithms can outperform a previous well-known method. The new approach subsumes and extends previous algorithms in a logical fashion     

Vector quantization of pitch information in Mandarin speech

A method of quantizing the shape of pitch contour segments of Mandarin speech by using orthogonal polynomial representation and vector quantization techniques is proposed. Only a very limited number of representative pitch contour patterns of words can be found in Mandarin conversation; therefore, pitch information can be represented by the shape and the length of the pitch contour segment word by word instead of frame by frame. An average bit rate of 0.78 b/frame (34.67 b/s) for voiced sounds was achieved. The method is a variable-rate coding scheme with an average delay of 317 ms