Uniform Scalar Dead Zone Quantizer Research Articles

Contribution to S-EMG Signal Compression in 1D by the Combination of the Modified Discrete Wavelet Packet Transform (MDWPT) and the Discrete Cosine Transform (DCT)

A new Modified Discrete Wavelets Packets Transform (MDWPT) based method for the compression of Surface EMG signal (s-EMG) data is presented. A Modified Discrete Wavelets Packets Transform (MDWPT) is applied to the digitized s-EMG signal. A Discrete Cosine Transforms (DCT) is applied to the MDWPT coefficients (only on detail coefficients). The MDWPT+ DCT coefficients are quantized with a Uniform Scalar Dead-Zone Quantizer (USDZQ). An arithmetic coder is employed for the entropy coding of symbol streams. The proposed approach was tested on more than 35 actuals S-EMG signals divided into three categories. The proposed approach was evaluated by the following parameters: Compression Factor (CF), Signal to Noise Ratio (SNR), Percent Root mean square Difference (PRD), Mean Frequency Distortion (MFD) and the Mean Square Error (MSE). Simulation results show that the proposed coding algorithm outperforms some recently developed s-EMG compression algorithms.

Comparison of Particle Swarm Algorithm and Ant Colony Algorithm in the Optimization of Uniform Quantizer

In this paper, we focuse on the study of parameter optimization problem of the uniform scalar dead zone quantizer (USDZQ).We used particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm for the optimization. We used the two kinds of optimization algorithm for ECG signal coding test.Those ECG signals were from the MIT-BIH arrhythmia database. At the end of this paper, the results of compression are compared.

2-Step Scalar Deadzone Quantization for Bitplane Image Coding

Modern lossy image coding systems generate a quality progressive codestream that, truncated at increasing rates, produces an image with decreasing distortion. Quality progressivity is commonly provided by an embedded quantizer that employs uniform scalar deadzone quantization (USDQ) together with a bitplane coding strategy. This paper introduces a 2-step scalar deadzone quantization (2SDQ) scheme that achieves same coding performance as that of USDQ while reducing the coding passes and the emitted symbols of the bitplane coding engine. This serves to reduce the computational costs of the codec and/or to code high dynamic range images. The main insights behind 2SDQ are the use of two quantization step sizes that approximate wavelet coefficients with more or less precision depending on their density, and a rate-distortion optimization technique that adjusts the distortion decreases produced when coding 2SDQ indexes. The integration of 2SDQ in current codecs is straightforward. The applicability and efficiency of 2SDQ are demonstrated within the framework of JPEG2000.

General Embedded Quantization for Wavelet-Based Lossy Image Coding

Embedded quantization is a mechanism employed by many lossy image codecs to progressively refine the distortion of a (transformed) image. Currently, the most common approach to do so in the context of wavelet-based image coding is to couple uniform scalar deadzone quantization (USDQ) with bitplane coding (BPC). USDQ+BPC is convenient for its practicality and has proved to achieve competitive coding performance. But the quantizer established by this scheme does not allow major variations. This paper introduces a multistage quantization scheme named general embedded quantization (GEQ) that provides more flexibility to the quantizer. GEQ schemes can be devised for specific decoding rates achieving optimal coding performance. Practical approaches of GEQ schemes achieve coding performance similar to that of USDQ+BPC while requiring fewer quantization stages. The performance achieved by GEQ is evaluated in this paper through experimental results carried out in the framework of modern image coding systems.

ECG compression using the context modeling arithmetic coding with dynamic learning vector–scalar quantization

Electrocardiogram (ECG) compression can significantly reduce the storage and transmission burden for the long-term recording system and telemedicine applications. In this paper, an improved wavelet-based compression method is proposed. A discrete wavelet transform (DWT) is firstly applied to the mean removed ECG signal. DWT coefficients in a hierarchical tree order are taken as the component of a vector named tree vector (TV). Then, the TV is quantized with a vector–scalar quantizer (VSQ), which is composed of a dynamic learning vector quantizer and a uniform scalar dead-zone quantizer. The context modeling arithmetic coding is finally employed to encode those quantized coefficients from the VSQ. All tested records are selected from the Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database. Statistical results show that the compression performance of the proposed method outperforms several published compression algorithms.

ECG compression using uniform scalar dead-zone quantization and conditional entropy coding

A new wavelet-based method for the compression of electrocardiogram (ECG) data is presented. A discrete wavelet transform (DWT) is applied to the digitized ECG signal. The DWT coefficients are first quantized with a uniform scalar dead-zone quantizer, and then the quantized coefficients are decomposed into four symbol streams, representing a binary significance stream, the signs, the positions of the most significant bits, and the residual bits. An adaptive arithmetic coder with several different context models is employed for the entropy coding of these symbol streams. Simulation results on several records from the MIT-BIH arrhythmia database show that the proposed coding algorithm outperforms some recently developed ECG compression algorithms.

Image coding based on wavelet transform and uniform scalar dead zone quantizer

In this paper, a new wavelet transform image coding algorithm is presented. The discrete wavelet transform (DWT) is applied to the original image. The DWT coefficients are firstly quantized with a uniform scalar dead zone quantizer. Then the quantized coefficients are decomposed into four symbol streams: a binary significance map symbol stream, a binary sign stream, a position of the most significant bit (PMSB) symbol stream and a residual bit stream. An adaptive arithmetic coder with different context models is employed for the entropy coding of these symbol streams. Experimental results show that the compression performance of the proposed coding algorithm is competitive to other wavelet-based image coding algorithms reported in the literature.

An overview of quantization in JPEG 2000

Quantization is instrumental in enabling the rich feature set of JPEG 2000. Several quantization options are provided within JPEG 2000. Part I of the standard includes only uniform scalar dead-zone quantization, while Part II allows both generalized uniform scalar dead-zone quantization and trellis coded quantization (TCQ). In this paper, an overview of these quantization methods is provided. Issues that arise when each of these methods are employed are discussed as well.