Delayed Decision Switched Prediction Multi-Stage LSF Quantization

A computationally efficient algorithm is proposed for modeling and coding the time-varying spectra of musical sounds. The aim is to encode individual data sets and not the statistical properties of the sounds. A given sequence of acoustic feature vectors is modeled by finding such a set of “states” (anchor points in the feature space) that the input data can be efficiently represented by interpolating between them. The achieved modeling accuracy for a database of musical sounds was approximately two times better than that of a conventional “vector quantization” model where the input data was k-means clustered and the input data vectors were then replaced by their corresponding cluster centroids. The computational complexity of the proposed algorithm as a function of the input sequence length T is O(TlogT).

For the large-volume ultraspectral sounder data, compression is desirable to save storage space and transmission time. To retrieve the geophysical paramters without losing precision the ultraspectral sounder data compression has to be lossless. Recently there is a boom on the use of graphic processor units (GPU) for speedup of scientific computations. By identifying the time dominant portions of the code that can be executed in parallel, significant speedup can be achieved by using GPU. Predictive partitioned vector quantization (PPVQ) has been proven to be an effective lossless compression scheme for ultraspectral sounder data. It consists of linear prediction, bit depth partitioning, vector quantization, and entropy coding. Two most time consuming stages of linear prediction and vector quantization are chosen for GPU-based implementation. By exploiting the data parallel characteristics of these two stages, a spatial division design shows a speedup of 72x in our four-GPU-based implementation of the PPVQ compression scheme.

Transform coding is commonly used in image processing algorithms to provide high compression ratios, often at the expense of processing time and simplicity of the system. We have recently proposed a pixel value prediction scheme in order to exploit adjacent pixel correlation, providing a low-complexity model for image coding. However, the proposed model was unable to reach high compression ratios retaining high quality of reconstructed image at the same time. In this paper we propose a new segmentation algorithm which further utilizes adjacent pixel correlation, provides higher compression ratios and it is based on application of Hadamard transform coding. Additional compression is provided by using vector quantization for a low number of quantization levels and by simplifying generalized Lloyd’s algorithm where the special attention is paid to determination of optimal partitions for vector quantization, making a fixed quantizer. The proposed method is quite simple and experimental results show that it ensures better or similar rate-distortion ratio for very low bit-rates, comparing to the other similar methods that are based on wavelet or curvelet transform coding and support or core vector machine application. Furthermore, the proposed method requires very low processing time since the proposed quantizers are fixed, much less than the required time for the aforementioned methods that we compare with as well as much less than the time required for fractal image coding. In the end, the appropriate discussion is provided comparing the results with a scheme based on linear prediction and dual-mode quantization.

A vector quantizer is a system for mapping a sequence of continuous or discrete vectors into a digital sequence suitable for communication over or storage in a digital channel. The goal of such a system is data compression: to reduce the bit rate so as to minimize communication channel capacity or digital storage memory requirements while maintaining the necessary fidelity of the data. The mapping for each vector may or may not have memory in the sense of depending on past actions of the coder, just as in well established scalar techniques such as PCM, which has no memory, and predictive quantization, which does. Even though information theory implies that one can always obtain better performance by coding vectors instead of scalars, scalar quantizers have remained by far the most common data compression system because of their simplicity and good performance when the communication rate is sufficiently large. In addition, relatively few design techniques have existed for vector quantizers. During the past few years several design algorithms have been developed for a variety of vector quantizers and the performance of these codes has been studied for speech waveforms, speech linear predictive parameter vectors, images, and several simulated random processes. It is the purpose of this article to survey some of these design techniques and their applications.

The partitioned predictive vector quantization (PPVQ) algorithm is known for its high compression ratio for lossless compression of the ultraspectral sounder data with high spatial and spectral resolutions. With the advent of the multicore technologies, parallelization of several parts of the algorithm has been explored in previous work using a compute unified device architecture (CUDA) aided environment on the Graphics Processing Unit (GPU). Recently the Intel Many Integrated Core (MIC) architecture on a coprocessor is introduced which shows promise in handling more divergent workloads as needed in PPVQ. Therefore we will explore the parallel performance of the MIC-aided implementation. With parallelization of the two most time-consuming modules of linear prediction and vector quantization in PPVQ, the total processing time of an AIRS granule can be compressed in less than 7.5 seconds which is equivalent to a speedup of ~8.8x. The use of MIC for PPVQ compression is thus promising as a low-cost and effective compression solution for ultraspectral sounder data for ground rebroadcast use.

Vector Quantization, Clustering, and Data Compression via Finite Mixture Models

The optimal centroidal Voronoi tessellations and the gersho's conjecture in the three-dimensional space

This paper describes an off-line segmentation-free handwritten Arabic words recognition system. The described system uses discrete HMMs with explicit state duration of various kinds (Gauss, Poisson and gamma) for the word classification purpose. After preprocessing, the word image is analyzed from right to left in order to extract from it a sequence of feature vectors. Then, vector quantization is applied to this sequence and its output is submitted to a HMMs classifier based on a likelihood criterion for identifying the word using the viterbi algorithm. Several experiments were performed using the IFN/ENIT benchmark database, they showed, on the one hand, a substantial improvement in the recognition rate when HMMs with explicit state duration of either discrete or continuous distribution are used instead of classical HMMs (i.e. with implicit state duration), on the other hand, the gamma distribution for the state duration, that have given the best recognition rate (91.23 % in top 2), seems more suitable for the HMMs based modeling of Arabic handwriting

Vector quantization is the process of encoding vector data as an index to a dictionary-or codebook-of representative vectors. Entropy-constrained vector quantizers (ECVQ) explicitly control the entropy of the output, and are superior to simple nearest-neighbor vector quantizers in terms of rate-distortion performances. ECVQ codebook design based on empirical data involves an expensive training phase in which a Lagrangian cost measure has to be minimized over the set of codebook vectors. In this paper, we describe two new general elimination rules allowing significant acceleration in the codebook design process. These rules use features of the codebook vectors in order to discard most of them as fast as possible during the search. Experimental results are presented on image block data. They show that those new rules perform slightly better than the previously known methods.

We propose the use of a low-complexity vector quantizer for coding data vectors at the focal plane of CMOS image sensors, directly from analog pixels samples prior to A/D conversion. To be suitable for focal-plane image compression applications, the encoder must have a very low transistor count. Without considering the implementation errors in DPCM preprocessing of the image data, a test image is compressed by the proposed vector quantizer with peak signal-to-noise ratio around 26 dB at 0.73 bpp. The vector quantizer requires 145 transistors for each block of 4x4 pixels, and Monte Carlo simulation results in Cadence/Spectre led to the same image compression results that had been achieved in theoretical predictions.

In video compression, some kernel functions such as block matching, discrete wavelet transform, vector quantization, etc., are of prime essential, but with a large amount of computation. Recently, many systolic array architectures have been designated for performing each of those functions in real time. In fact, many kernel functions contain the similar computational procedure. If we dissect these functions into the basic matrix-vector product forms, a unified design for them becomes feasible. In this brief, by carefully extracting the common computation component, a unified one-dimensional systolic array design that can perform at least the above three functions is presented. In this design, the input data is serial-in to save the amount of pins required, and the data flow are carefully arranged to simplify the interconnection between computation components. When 64 registers are in the on-chip memory, our design can perform three typical functions: (1) the full-search block matching with block size 16/spl times/16 and the search range (-8,7); (2) the 2-D Harr transform with block size 8/spl times/8; and (3) the vector quantization with input vector size 4/spl times/4 and codebook size 256. Since the unified architecture reduces hardware costs, and has a regular hardware structure, it is suited for VLSI implementation for video/image compression applications that require all the functions.

The 7 kHz audio bandwidth is increasingly being considered for natural quality communications, especially for binaural, hands-free listening situations such as in teleconferencing and digital AM broadcasting. Based on detailed performance comparisons of several alternative designs, we shortlisted an ACELP-based 7 kHz coder design for performance optimization. This paper presents several low-complexity design strategies which significantly reduce the computational complexity of the encoder and decoder while not compromising on the reconstructed signal quality. These include: (a) open-loop pitch estimation and reduced search adaptive codebook; (b) open-loop pitch estimation based on pre-search in downsampled signal domain; (c) LSF quantization using a combination of scalar and vector quantization; (d) a novel reduced complexity search of algebraic codebook; and (e) efficient postfiltering in the decoder. The computational complexity of the encoder and decoder modules of three ACELP coders with different bit allocations are compared.

Essentials of the self-organizing map

The implementation of an intermediary-proxy is a common approach to the problem of network heterogeneity in the Internet infrastructure. Due to the hypertext nature of the most popular Internet application-the World Wide Web, image compression is considered to be one of the fundamental functions of such a proxy. It has been observed that most images embedded into Web documents are of 'information-delivery' type, so an algorithm intended for their compression has to satisfy some specific requirements. First, in order to support network (bandwidth) constraints for an arbitrary case, the algorithm should be inherently adaptive, i.e. able to provide a wide range of compression rates. Second, as dealing with images that are integral parts of an interactive application (such as a Web browser), the algorithm should be capable of preserving a sufficient level of image semantics according to the quality standards of human perception. The vector quantization (VQ) technique, in its general form, is proven to satisfy the first requirement. On the other hand, a modified adaptive resonance (modified ART2) learning algorithm (which we employ in this paper) more properly belongs to the family of NN algorithms whose main goal is the discovery of input data clusters, without considering their actual size. This feature makes the modified ART2 algorithm satisfy the second requirement. Thus, the discussion and results presented are intended to show that modified ART2 underlying the general VQ procedure is an appropriate techniques for image compression purposes in a bandwidth-constrained environment.

In this correspondence, a modified version of Hunt's (1980) image model is used to interpret the distribution of image data vectors. The model suggests that the diagonal line of the coordinates system is a good approximation of the principal axis of the image data vector set. The validity of the model is supported by experiments. Following this suggestion, an effective nearest neighbor search method for vector quantization of image data is developed. The method is based on partitioning the vector space using hyperplanes which are perpendicular to the diagonal direction of the coordinate system. The validity of the method is assessed by analyzing its complexity and comparing its performance to those of existing algorithms on a number of images.