Abstract: The implementation of image processing engines in visual sensor nodes has been a major concern in the development of wireless multimedia sensor networks in a hardware constrained environment. In this paper, a review on eight popular image compression algorithms is presented. After conducting a comprehensive evaluation, it is found that Set-Partitioning in Hierarchical Trees (SPIHT) wavelet-based image compression is the most suitable hardware implemented image compression algorithm in wireless sensor networks due to its high compression efficiency and its simplicity in coding procedures.

Abstract: This paper presents an approach for human action recognition by finding the discriminative key frames from a video sequence and representing them with the distribution of local motion features and their spatiotemporal arrangements. In this approach, the key frames of the video sequence are selected by their discriminative power and represented by the local motion features detected in them and integrated from their temporal neighbors. In the key frame’s representation, the spatial arrangements of the motion features are captured in a hierarchical spatial pyramid structure. By using frame by frame voting for the recognition, experiments have demonstrated improved performances over most of the other known methods on the popular benchmark data sets. Recognizinghumanactionfromimagesequencesis an appealingyet challengingproblem in computer vision with many applications including motion capture, human-computer interaction, environment control, and security surveillance. In this paper, we focus on recognizing the activities of a person in an image sequence from local motion features and their spatiotemporal arrangements. Our approach is motivated by the recent success of “bag-of-words” model for general object recognition in computer vision[21, 14]. This representation, which is adapted from the text retrieval literature, models the object by the distribution of words from a fixed visual code book, which is usually obtained by vector quantization of local image visual features. However, this method discards the spatial and the temporal relations among these visual features, which could be helpful in object recognition. Addressing this problem, our approach uses a hierarchical representation for the key frames of a given video sequence to integrate information from both the spatial and the temporal domains. We first apply a spatiotemporal feature detector to the video sequence and obtain the local motion features. Then we generate a visual word code book by quantization of the local motion features and assign a word label to each of them. Next we select key frames of the video sequence by their discriminative power. Then, for each key frame, we integrate the visual words from its nearby frames, divide the key frame spatially into finer subdivisions and compute in each cell the histograms of the visual words detected in this key frame and its temporal neighbors. Finally, we concatenate the histograms from all cells and use

Abstract: The lossy JPEG compression scheme employs a quantization table that controlstheamountofcompressionachieved. Becausedifferentcamerastypicallyemploy different tables, a comparison of an image’s quantization scheme to a database of known cameras affords a simpletechniqueforconfirmingordenying an image’s source. This report describes the analysis of quantization tables extracted from 1,000,000 images downloaded from Flickr.com.

Abstract: In this paper we present a very simple and yet effective algorithm to generate codebook. The algorithm uses sorting method to generate codebook and the codevectors are obtained by using median approach. The proposed algorithm was experimented on six different images each of size 512 x 512 and four different codebooks of sizes 128, 256, 512 and 1024 are generated. The proposed algorithm is found to be much faster than the LBG and KPE algorithm. The performance of this algorithm is better than LBG and KPE algorithms considering MSE, PSNR and execution time. The proposed algorithm gives less MSE as compared to the LBG for the codebooks of sizes 128, 256, 512 & 1024 respectively. It also gives higher PSNR as compared to LBG for the codebooks of various sizes.

Abstract: We consider secret key (SK) generation by two terminals, each of which observes an i.i.d. source which is correlated with the other; the two sources are jointly Gaussian with known distribution. The terminals are then allowed to communicate with each other, possibly interactively in many rounds and without rate restrictions, over a noiseless two-way public channel. Randomization is permitted at each terminal. The goal is for the terminals to generate a SK comprising discrete common randomness which is concealed from an eavesdropper that has access to the public interterminal communication. We establish that this maximum rate of SK generation, i.e., SK capacity, is, as expected, the per-symbol mutual information I of the correlated sources. Our main technical contribution is a new scheme for achieving SK capacity using structured codes. In our scheme, vector quantization at rate R nats/source symbol of the source at terminal 1 is performed by employing nested lattice codes with dithering. Then, a SK is generated by the terminals from the quantized random sequence at terminal 1 and the (unquantized) Gaussian source at terminal 2, using both lossy and lossless data compression techniques. Our algorithm, using a rate-R lattice quantizer, achieves a maximum SK rate of 1/2 log 1/(e-2I+(1-e-2I)e-2R), which tends to the SK capacity I with increasing R. Thus, our result also specifies the associated tradeoff between quantization rate R and the maximum achievable SK rate. This tradeoff is shown to be optimum among a certain restricted class of schemes for SK generation that involve quantization at rate R of a Gaussian source at one terminal.

Abstract: Clustering problems appear in a wide range of unsupervised classification applications such as pattern recognition, vector quantization, data mining and knowledge discovery. The k-means algorithm is one of the most widely used clustering techniques. Unfortunately, k-means is extremely sensitive to the initial choice of centers and a poor choice of centers may lead to a local optimum that is quite inferior to the global optimum. This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Ant Colony Optimization (ACO) and Simulated Annealing (SA), called ACO-SA, for cluster analysis. The performance is evaluated through several benchmark data sets. The simulation results show that the proposed algorithm outperforms the previous approaches such as SA, ACO and k-means for partitional clustering problem.

Abstract: Mostly transforms are used for speech data compressions which are lossy algorithms. Such algorithms are tolerable for speech data compression since the loss in quality is not perceived by the human ear. However the vector quantization (VQ) has a potential to give more data compression maintaining the same quality. In this paper we propose speech data compression algorithm using vector quantization technique. We have used VQ algorithms LBG, KPE and FCG. The results table shows computational complexity of these three algorithms. Here we have introduced a new performance parameter Average Fractional Change in Speech Sample (AFCSS). Our FCG algorithm gives far better performance considering mean absolute error, AFCSS and complexity as compared to others. Keywords—Vector Quantization, Data Compression, Encoding,, Speech coding.

Abstract: This paper presents a novel unified framework for compression and decision making by using artificial neural networks. The proposed framework is applied to medical images like magnetic resonance (MR), computer tomography (CT) head images and ultrasound image. Two artificial neural networks, Kohonen map and incremental self-organizing map (ISOM), are comparatively examined. Compression and decision making processes are simultaneously realized by using artificial neural networks. In the proposed method, the image is first decomposed into blocks of 8x8 pixels. Two-dimensional discrete cosine transform (2D-DCT) coefficients are computed for each block. The dimension of the DCT coefficients vectors (codewords) is reduced by low-pass filtering. This way of dimension reduction is known as vector quantization in the compression scheme. Codewords are the feature vectors for the decision making process. It is observed that the proposed method gives higher compression rates with high signal to noise ratio compared to the JPEG standard, and also provides support in decision-making by performing segmentation.

Abstract: In this paper we propose segmentation approach based on Vector Quantization technique. Here we have used Kekre’s fast codebook generation algorithm for segmenting low-altitude aerial image. This is used as a preprocessing step to form segmented homogeneous regions. Further to merge adjacent regions color similarity and volume difference criteria is used. Experiments performed with real aerial images of varied nature demonstrate that this approach does not result in over segmentation or under segmentation. The vector quantization seems to give far better results as compared to conventional on-the-fly watershed algorithm. Keywords—Image Segmentation,, Codebook, Codevector, data compression, Encoding

Abstract: In this paper, Bayesian hypothesis testing is investigated when the prior probabilities of the hypotheses, taken as a random vector, are quantized. Nearest neighbor and centroid conditions are derived using mean Bayes risk error (MBRE) as a distortion measure for quantization. A high-resolution approximation to the distortion-rate function is also obtained. Human decision making in segregated populations is studied assuming Bayesian hypothesis testing with quantized priors.

Abstract: Modeling audio signals by the long-term statistical distribution of their local spectral features - often denoted as bag of frames approach (BOF) - is a popular and powerful method to describe audio content. While modeling the distribution of local spectral features by semi-parametric distributions (e.g. Gaussian Mixture Models) has been studied intensively, we investigate a non-parametric variant based on vector quantization (VQ) in this paper. The essential advantage of the proposed VQ approach over stateof-the-art similarity measures is that the proposed audio similarity metric forms a normed vector space. This allows for more powerful search strategies, e.g. KD-Trees or Local Sensitive Hashing (LSH), making content-based audio similarity available for even larger music archives. Standard VQ approaches are known to be computationally very expensive; to counter this problem, we propose a multi-level clustering architecture. Additionally, we show that the multi-level vector quantization approach (ML-VQ), in contrast to standard VQ approaches, is comparable to state-ofthe-art frame-level similarity measures in terms of quality. Another important finding w.r.t. the ML-VQ approach is that, in contrast to GMM models of songs, our approach does not seem to suffer from the recently discovered hub problem.

Abstract: The overdrive technique has been popularly used to alleviate the motion blur on liquid-crystal display (LCD) by shortening the response time of the liquid crystal. However, this technique requires a large frame buffer to store the previous frame as a reference. In this paper, we propose a novel block truncation coding based on the vector quantizer for the color image compression in LCD overdrive. Due to the constant output bit-rate and the low computational complexity, the proposed method is suitable for the hardware implementation in LCD overdrive. Experimental results show that the proposed method achieves higher compression ratio as well as better visual quality as compared with the conventional methods.

Abstract: In this paper, a novel method for continuous human movement recognition based on fuzzy vector quantization (FVQ) and linear discriminant analysis (LDA) is proposed. We regard a movement as a unique combination of basic movement patterns, the so-called dynemes. The proposed algorithm combines FVQ and LDA to discover the most discriminative dynemes as well as represent and discriminate the different human movements in terms of these dynemes. This method allows for simple Mahalanobis or cosine distance comparison of not aligned human movements, taking into account implicitly time shifts and internal speed variations, and, thus, aiding the design of a real-time continuous human movement recognition algorithm. The effectiveness and robustness of this method is shown by experimental results on a standard dataset with videos captured under real conditions, and on a new video dataset created using motion capture data.

Abstract: Researchers have been investigating the use of artificial neural networks (NNs) in the application of control chart pattern (CCP) recognition with encouraging results in recent years. Most of the NN models in this field are designed to be used in uncorrelated processes where the process data are independent. Unfortunately, the prerequisite of data independence is not even approximately satisfied in many manufacturing processes. To the best of the author's knowledge, no research results have been published to date on the application of NNs for CCP recognition in autocorrelated processes. This work first shows that autocorrelation in process data greatly affects the performance of NN-based CCP recognizers developed with independent data and then presents a learning vector quantization network-based system that can effectively recognize CCPs in real-time for processes with various levels of autocorrelation. The system performance is evaluated in terms of the classification rate and the average run length. An...

Abstract: We proposed a vector quantization (VQ) with variable block size using local fractal dimensions (LFDs) of an image. A VQ with variable block size has so far been implemented using a quad tree (QT) decomposition algorithm. QT decomposition carries out image partitioning based on the homogeneity of local regions of an image. However, we think that the complexity of local regions of an image is more essential than the homogeneity, because we pay close attention to complex region than homogeneous region. Therefore, complex regions are essential for image compression. Since the complexity of regions of an image is quantified by values of LFD, we implemented variable block size using LFD values and constructed a codebook (CB) for a VQ. To confirm the performance of the proposed method, we only used a discriminant analysis and FGLA to construct a CB. Here, the FGLA is the algorithm to combine generalized Lloyd algorithm (GLA) and the fuzzy k means algorithm. Results of computational experiments showed that this method correctly encodes the regions that we pay close attention. This is a promising result for obtaining a well-perceived compressed image. Also, the performance of the proposed method is superior to that of VQ by FGLA in terms of both compression rate and decoded image quality. Furthermore, 1.0bpp and more than 30dB in PSNR by a CB with only 252 code-vectors were achieved using this method.

Abstract: This paper proposes an efficient two-description image coding technique. The two side descriptions of an image are generated by quincunx subsampling. The decoding from any side description is done by an interpolation process that exploits sample correlation. Although the quincunx subsampling is a natural choice for the best use of sample correlations in image multiple-description coding (MDC), each side description is not amenable to existing image coding techniques because the pixels are not aligned rectilinearly. We show how this difficulty can be overcome by an adaptive directional lifting (ADL) transform that is particularly suitable for decorrelating samples on the quincunx lattice. The ADL transform can be embedded into JPEG 2000 to construct a practical MD image encoder. Experimental results demonstrate that the proposed image MDC scheme can achieve good coding performance.

Abstract: In this work, we further develop the multidimensional multiscale parser (MMP) algorithm, a recently proposed universal lossy compression method which has been successfully applied to images as well as other types of data, as video and ECG signals. The MMP is based on approximate multiscale pattern matching, encoding blocks of an input signal using expanded and contracted versions of patterns stored in a dictionary. The dictionary is updated using expanded and contracted versions of concatenations of previously encoded blocks. This implies that MMP builds its own dictionary while the input data is being encoded, using segments of the input itself, which lends it a universal flavor. It presents a flexible structure, which allows for easily adding data-specific extensions to the base algorithm. Often, the signals to be encoded belong to a narrow class, as the one of smooth images. In these cases, one expects that some improvement can be achieved by introducing some knowledge about the source to be encoded. In this paper, we use the assumption about the smoothness of the source in order to create good context models for the probability of blocks in the dictionary. Such probability models are estimated by considering smoothness constraints around causal block boundaries. In addition, we refine the obtained probability models by also exploiting the existing knowledge about the original scale of the included blocks during the dictionary updating process. Simulation results have shown that these developments allow significant improvements over the original MMP for smooth images, while keeping its state-of-the-art performance for more complex, less smooth ones, thus improving MMP's universal character.

Abstract: A flexible and low-complexity entropy-constrained vector quantizer (ECVQ) scheme based on Gaussian mixture models (GMMs), lattice quantization, and arithmetic coding is presented. The source is assumed to have a probability density function of a GMM. An input vector is first classified to one of the mixture components, and the Karhunen-Loeve transform of the selected mixture component is applied to the vector, followed by quantization using a lattice structured codebook. Finally, the scalar elements of the quantized vector are entropy coded sequentially using a specially designed arithmetic coder. The computational complexity of the proposed scheme is low, and independent of the coding rate in both the encoder and the decoder. Therefore, the proposed scheme serves as a lower complexity alternative to the GMM based ECVQ proposed by Gardner, Subramaniam and Rao. The performance of the proposed scheme is analyzed under a high-rate assumption, and quantified for a given GMM. The practical performance of the scheme was evaluated through simulations on both synthetic and speech line spectral frequency (LSF) vectors. For LSF quantization, the proposed scheme has a comparable performance to at rates relevant for speech coding (20-28 bits per vector) with lower computational complexity.

Abstract: In this paper, we exploit a recently introduced coding algorithm called multidimensional multiscale parser (MMP) as an alternative to the traditional transform quantization-based methods. MMP uses approximate pattern matching with adaptive multiscale dictionaries that contain concatenations of scaled versions of previously encoded image blocks. We propose the use of predictive coding schemes that modify the source's probability distribution, in order to favour the efficiency of MMP's dictionary adaptation. Statistical conditioning is also used, allowing for an increased coding efficiency of the dictionaries' symbols. New dictionary design methods, that allow for an effective compromise between the introduction of new dictionary elements and the reduction of codebook redundancy, are also proposed. Experimental results validate the proposed techniques by showing consistent improvements in PSNR performance over the original MMP algorithm. When compared with state-of-the-art methods, like JPEG2000 and H.264/AVC, the proposed algorithm achieves relevant gains (up to 6 dB) for nonsmooth images and very competitive results for smooth images. These results strongly suggest that the new paradigm posed by MMP can be regarded as an alternative to the one traditionally used in image coding, for a wide range of image types.

Abstract: This paper employs a high resolution quantization framework to study the effects of finite-rate feedback of the channel state information (CSI) on the performance of multiple-input-multiple-output (MIMO) systems over independently and identically distributed (i.i.d.) Rayleigh flat fading channels. The contributions of this paper are twofold. First, we extend the general distortion analysis of vector quantizers to deal with complex source variables. Necessary and sufficient conditions that guarantee a concise high-resolution distortion analysis in the complex domain is presented. Second, as an application of the proposed complex distortion analysis, tight lower bounds on the capacity loss due to the finite-rate channel quantization are provided for MIMO systems employing a fixed number of equal power spatial beams. Based on the obtained closed-form analytical results, it is shown that the system capacity loss decreases exponentially as the ratio of the quantization rate to the total degrees of freedom of the channel state information to be quantized. Moreover, MIMO CSI-quantizers using mismatched codebooks that are only optimized for high-signal-to-noise ratio (SNR) and low-SNR regimes are also investigated to quantify the penalties incurred by the use of mismatched codebooks. In addition, the analysis is extended to deal with MIMO systems using multi-mode spatial multiplexing transmission schemes with finite-rate CSI feedback. Finally, numerical and simulation results are presented which confirm the tightness of the derived theoretical distortion bounds.