Adaptive quantization scheme using dual-interleaved scalar quantizers for image/video coding

We consider distributed parameter estimation using quantized observations in wireless sensor networks (WSN) operating in a noisy channel environment. Due to bandwidth constraints, each sensor quantizes its local observation into one bit of information. Previously, adaptive quantization(AQ) schemes were developed under the assumption of perfect communication links between the sensors and the fusion center (FC). In this paper we propose an adaptive quantization scheme for a WSN with channel links modeled as binary erasure channels. A first-order Hidden Markov Model (HMM) framework is introduced to model the adaptive quantization scheme. The introduction of a HMM framework aids in the systematic design of an estimator. To address the significant problem of bit erasures, we propose an Expectation-Maximization (EM) based estimator. Theoretical closed form solutions for the Cramer-Rao lower bounds are developed for the proposed estimation problem under certain assumptions. We analyze the performance of the proposed quantization scheme and estimator under different criteria. Numerical simulation results are shown for the proposed adaptive quantization and EM parameter estimation scheme under different scenarios. The simulation results indicate that the proposed quantization scheme and estimator are robust and can provide superior performance for erasure rates up to 10 %.

In order to improve the accuracy and the convergence rate of distributed consensus under quantized communication, in the paper, based on adaptive quantization scheme, we propose the fast second-order distributed consensus algorithm. We analyze the convergence performance. The fast second-order distributed consensus with adaptive quantization achieves a consensus in a mean square sense, and the consensus is equal to the average of the initial states. Simultaneously, Simulations are done about the fast second-order distributed consensus based on adaptive quantization. Results show that the fast second-order distributed consensus algorithm based on adaptive quantization can reach an average consensus, and its convergence rate is higher than that of the first-order adaptive quantized distributed consensus algorithm, moreover, the mean square errors are smaller within the finite steps.

We propose to extend some of the ideas of adaptive lossless compression to design an adaptive quantization algorithm. Noting that the performance of an arithmetic coder is as good as its estimation of the statistics of the input, we split our quantizer into two building blocks: model estimation and quantizer design. The main idea is that, as long as the model estimation manages to track down the changes in source statistics, a standard quantizer design technique which assumes that the source follows the estimated model can be used. As an example of this type of design we study an adaptive scalar quantization scheme where we impose the restriction that no side information can be sent, i.e. encoder and decoder must perform their adaptation based on the quantized information.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

We propose in this paper a variable-coefficient fixed-length (VCFL) coding scheme for wavelet-based image transmission over noisy channels. When an image is transmitted through noisy channel with high throughput, both image compression and error-resistant coding scheme need to be considered. In this approach, an image is first decomposed into subbands by wavelet transform and quantized using an adaptive quantization scheme. The adaptive quantization is adaptive to both the frequency characteristics and the spatial constraints based on Gibbs random field. The traditional variable length entropy coding schemes, such as Huffman coding or arithmetic coding, and the fixed length coding such as LZW are usually very sensitive to channel noise for image transmission applications. Even with the insertion of synchronization symbols, they still cannot be directly employed without additional error correction/detection coding. To overcome the difficulty of image transmission over noisy channels, we propose to code the quantized subband coefficients with the VCFL scheme. This coding scheme attempts to keep the balance between redundancy removal, synchronization detection and error resilience. Part of the codebook is field based on the observation of the coefficient spatial distribution patterns in each subbands to alleviate the transmission of the codebook. The remaining code positions within the fixed length codebook can be utilized to combat channel errors by carefully arranging the code positions such that the codes with biggest transition cost will have the biggest Hamming distance. These positions can laos be filled with other frequently appeared coefficient composition sequences to achieve higher compression ratio. Experimental results of image transmission over noisy channels are reported to show the promising potential of the proposed coding scheme.

Range estimation is essential in many sensor network localization algorithms. Although wireless sensor systems usually have available received signal strength indication (RSSI) readings, this information has not been effectively used in the existing localization algorithms. In this paper, we present a sensor network localization algorithm based on a sorted RSSI quantization scheme that can improve the range estimation accuracy when distance information is not available. The range level used in the quantization process can be decided by each node using an adaptive quantization scheme. The new algorithm can be implemented in a distributed way and has significant improvement over existing range-free algorithms. The performance advantage for various sensor networks is shown with experimental results from our extensive simulation with a more realistic radio model.

In wireless sensor networks, due to the bandwidth constraint, the distributed nodes (DNs) might only provide binary representatives of the source signal, and then transmit them to the central node (CN). In this paper, we consider the joint estimation of signal amplitude and background noise variance from binary observations over decentralized channels. We first analyze the Cram&#x00E9;r&#x2013;Rao lower bounds (CRLBs) of the parameters of interest and develop a quasilinear estimator (QLE), in which the desirable estimates can be obtained from several intermediate parameters linearly. Next, we consider a more realistic situation where the decentralized channel is noisy during the data transmission. Based on the error propagation model, the asymptotic analysis shows that the performance of the proposed QLE is mainly dominated by the thresholds of the quantizers, which encourages us to adopt a correlated quantization (CQ) scheme by exploiting the spatial correlation among background noises/channel noises. To ease the implementation of QLE in practice, an adaptive quantization (AQ) scheme is also proposed so as to obtain reasonable selections of the required thresholds. Finally, numerical simulations are provided to validate our theoretical findings.

Vector quantization is a new coding technique which has enjoyed much success in its brief history. Its most attractive features are a high compression ratio and a simple decoder. Thus, it shows great promise in applications using a single-encoder with a multiple decoder such as videotext and archiving. However, some problems have arisen, most notably edge degradation, the so-called "block effect". A novel method is described in this paper which alleviated this problem without much increase in computational effort. An index has been devised, the activity index, based upon measurements on the input image data; it is used to classify image areas into two groups, active and nonactive. For nonactive areas, large block size is used, while for active areas the block size is small. Two codebooks are generated corresponding to each of the two groups of blocks formed. Using this adaptive vector quantization scheme the results obtained show that the edge features are well preserved upon reconstruction at the decoder.

In this paper, we present a secret key distribution protocol without resorting to a key management infrastructure targeting at providing a low-complexity distributed solution to wireless network. The proposed scheme extracts a secret key from the random fluctuation of wireless channels. By exploiting time division duplexing transmission, two legitimate users, Alice and Bob can have highly correlated channel gains due to channel reciprocity, and a pair of random bit sequences can be generated by quantizing the channel gains. We propose a novel adaptive quantization scheme that adjusts quantization thresholds according to channel variations and reduces the mismatch probability between generated bit sequences by Alice and Bob. BCH codes, as a low-complexity and pratical approach, are also employed to correct the mismatches between the pair of bit sequences and produce a secret key shared by Alice and Bob. To maximize the secret key extraction rate, the parameters, quantization levels and code rates of BCH codes are jointly optimized.

In digital signal processing in the transform domain, it is important to study the effects of quantization in the transform domain on the signal reconstructed by inverse transformation. Adaptive quantization schemes are developed earlier for Fourier transform domain and Hadamard transform domain, using the knowledge that in the transform domain adaptive set-dependent bounds can be obtained on the sample amplitudes. This paper discusses various pre-processing and post-processing techniques that reduce the errors and favourably change the distribution of errors in the reconstructed signal or reduce the effective bit-rate without affecting the errors in the reconstructed signal.

This paper proposes a distributed incremental quantization and estimation scheme by which each sensor can make a maximum likelihood estimation (MLE) of the unknown parameter based on its local observations and the quantized messages transmitted by the previous sensor. We derive the upper bound of the estimation mean squared error (MSE) of the proposed scheme, and propose the bit allocation algorithms, which minimize the total required bandwidth while ensuring a given MSE performance. Simulation results demonstrate that the proposed scheme requires much less bandwidth, but achieves around 40% MSE reduction compared with the distributed adaptive quantization and estimation scheme.

This study presents a transform-domain distributed video coding (DVC) system with a rate–distortion (R–D)-based Adaptive QuanTisation (AQT) scheme. In the proposed system, the transform-domain Wyner–Ziv frame is divided into partitions and is adaptively quantised based on estimated local R–D characteristics for each partition. The R–D estimation is performed based on a correlation model between the original source information and the side information and can be applied at the decoder without adding complexity to the encoder. Coding results and comparisons with existing DVC schemes and with H.264/AVC interframe and intraframe coding are presented to illustrate the performance of the proposed system.

The quantizer plays an important role not only in traditional video compression standards but also in the Wyner-Ziv coding, which can be thought to consist of a quantizer followed by a Slepian-Wolf encoder. In this context, this paper proposes a frame-level adaptive nested scalar quantization scheme for distributed video coding (DVC). At the encoder the absolute frame difference between current Wyner-Ziv frame and previous key frame is utilized to denote the statistical dependency, and then the adaptive quantizer stpesize is decided according to threshold and sent to decoder. The Wyner-Ziv frame is decoded and reconstructed based on the received parameter and the side information. It is shown that at a possible cost of higher error rate of channel code, the proposed adaptive quantization scheme is a potential way to lower the quantization error and on the whole improve the rate distortion performance, especially for video with low motion.

This paper presents a novel adaptive vector quantisation scheme based on the SOFM neural network. All adaptation is performed directly from the quantised image with no explicit adaptation information transmitted or stored. Thus the network learns an input distribution it has never actually seen. Training sets are generated from the received image by scaling the image to approximate the statistics of the original image and selecting blocks in such a way as to capture edges and other image features. This data is fed to a SOFM neural network to update the codebook. A new method is also presented for ensuring that all neurons are well used, by estimating directly from the quantised image how much distortion each neuron introduces. The ability of this scheme to adapt successfully is verified by simulation.

We present in this paper a study of medical image compression based on an adaptive quantization scheme capable of preserving clinically useful structures appeared in the given images. We believe that how accurate can a compression algorithm preserve these structures is a good measure of image quality after compression since many image-based diagnoses are based on the position and appearance of certain structures. With wavelet decomposition, we are able to investigate the image features at different scale levels that correspond to certain characteristics of biomedical structures contained in the medical images. An adaptive quantization algorithm based on clustering with spatial constraints is then applied to the high frequency subbands. The adaptive quantization enables us to selectively preserve the image features at various scales so that desired details of clinically useful structure are preserved during the process of compression, even at a low bit rate. Preliminary results based on real medical images suggest that this clustering-based adaptive quantization, combined with wavelet decomposition, is very promising for medical image compression with structure-preserving capability.

This paper presents an adaptive quantization scheme for the reduction of the percentage error in the output of the quantizer. The improvement in the system performance is illustrated by simulating a uniform step and an adaptive quantizer on a digital computer and comparing their outputs. A significant improvement in the output is achieved by using the suggested scheme.