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L1-norm-based coding for lattice vector quantization

L1-norm-based coding for lattice vector quantization

引用

Asia-Pacific signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Wisarn Patchoo Thomas R. Fischer Curtis Maddex School of Engineering Bangkok University Pathumthani Thailand School of Electrical Engineering and Computer Science Washington State University Pullman WA USA

A lattice vector quantization encoding method is developed based on ℓ 1 -norm-based enumeration and bit-plane coding. The algorithm is implemented for the cubic lattice, can handle arbitrary vector dimension, and is suitable for transform, subband, or wavelet coding applications. Moreover, the algorithm can possibly extend to other binary lattices.

关键词： Lattices Vectors Transforms signal to noise ratio Channel coding image coding

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image deconvolution under poisson noise using sparse representations and proximal thresholding iteration

Image deconvolution under poisson noise using sparse represe...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： F. -X. Dupe M. J. Fadili J.-L. Starck GREYC UMR CNRS 6072 Caen France DAPNIA/SEDI-SAP CEA-Saclay Gif-sur-Yvette France

We propose an image deconvolution algorithm when the data is contaminated by Poisson noise. The image to restore is assumed to be sparsely represented in a dictionary of waveforms such as the wavelet or curvelet transform. Our key innovations are: First, we handle the Poisson noise properly by using the Anscombe variance stabilizing transform leading to a non-linear degradation equation with additive Gaussian noise. Second, the deconvolution problem is formulated as the minimization of a convex functional with a data-fidelity term reflecting the noise properties, and a non-smooth sparsity-promoting penalties over the image representation coefficients (e.g. l 1 -norm). Third, a fast iterative backward-forward splitting algorithm is proposed to solve the minimization problem. We derive existence and uniqueness conditions of the solution, and establish convergence of the iterative algorithm. Experimental results are carried out to show the striking benefits gained from taking into account the Poisson statistics of the noise. These results also suggest that using sparse-domain regularization may be tractable in many deconvolution applications, e.g. astronomy or microscopy.

关键词： Deconvolution Additive noise Gaussian noise Iterative algorithms image restoration Dictionaries wavelet transforms Technological innovation Degradation Nonlinear equations

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A Rate-Constrained Pre-Compression Bit Allocation Algorithm for Memory Constrained Embedded applications of JPEG2000

A Rate-Constrained Pre-Compression Bit Allocation Algorithm ...

引用

IEEE International conference on signal processing and Communications, ICSPC

作者： Alireza Aminlou Amir Naghdinezhad Mahmoud Reza Hashemi Omid Fatemi Multimedia Processing Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

The execution order of the main components in a JPEG2000 encoder, i.e. the coding sequence, plays a significant role in its performance, implementation cost and memory requirement. The coding sequence is mainly affected by the bit allocation strategy. Existing strategies either require a large working memory or can not be used in rate constrained applications. In this paper, an improved pre-compression bit allocation algorithm for rate-constrained applications of JPEG2000 is proposed. Using the proposed method with multi-layered coding, the memory requirement of a JPEG2000 system is reduced by more than 80%. This is at the expense of a quality degradation of merely 0.47 dB in average, for higher compression ratios of 50 to 100.

关键词： Bit rate image coding Discrete wavelet transforms signal processing algorithms Transform coding Video coding Rate distortion theory Application software Laboratories Embedded computing

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Classification of human heart signals by novel feature extraction techniques for rescue application

Classification of human heart signals by novel feature extra...

引用

International conference on image Information processing

作者： Rajesh Saha Ratnesh Kumar Singh Rajeev Kumar Ghanapriya Singh Tushar Goel Pankaj Kumar Pal NIT Uttarakhand Srinagar Garhwal India

In the paper, the methodology of detecting the heart rate signal wirelessly with regards to use it as an applications as an emerging tool for health care in emergency and surveillance application is presented. A novel feature extraction technique is presented to detect human heart signal or electrocardiogram (ECG) signals wirelessly and classify the signal as normal or abnormal. By the analysis of the heart rate signal the presence of an alive human person below the debris can be detected. After detection of heart rate signals via sensors, the challenge lies in classifying the human heart rate signals as normal or abnormal. Heart signals or Electrocardiogram (ECG) is the primary signal used to diagnose and detection of heart disease abnormality. In this work, ECG signals are de-noised and then analyzed to extract feature from it by a novel technique which is combining Discrete wavelet Transform (DWT) and nonlinear, linear statistical feature. To verify the efficiency of extracted feature, here used various Machine Learning (ML) methods namely as Decision Trees (DT), Discriminant Analysis (DA), Logistic Regression (LR), Nearest Neighbor (KNN), Support Vector Machine (SVM) and Ensemble have been used as a classifiers and their results are compared. KNN reports the lowest accuracy of 81.60% on MIT- BIH Arrythmia Database. SVM and Ensemble reports the highest accuracy of 97.40%.

关键词： Electrocardiography Feature extraction Heart Discrete wavelet transforms Databases Sensors

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Oversampled Inverse Complex Lapped Transform Optimization

Oversampled Inverse Complex Lapped Transform Optimization

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jerome Gauthier Laurent Duval Jean-Christophe Pesquet Institut Français du Pétrole IFP Rueil-Malmaison Cedex France Institut Gaspard Monge and UMR CNRS 8049 Université de Marne La Vallée Marne La Vallée

When an oversampled FIR filter bank structure is used for signal analysis, a main problem is to guarantee its invertibility and to be able to determine an inverse synthesis filter bank. As the analysis scheme corresponds to a redundant decomposition, there is no unique inverse filter bank and some of the solutions can lead to artifacts in textured image filtering applications. In this paper, the flexibility in the choice of the inverse filter bank is exploited to find the best-localized impulse responses. The design is performed by solving a constrained optimization problem which is reformulated in a smaller dimensional space. Application to seismic data clearly shows the improvements brought by the optimization process.

关键词： Finite impulse response filter Filter bank Filtering Discrete cosine transforms signal analysis signal synthesis Design optimization wavelet transforms image analysis image texture analysis

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Robust EZW image transmission scheme using distributed-alamouti codes in relay networks

Robust EZW image transmission scheme using distributed-alamo...

引用

International conference on signal processing and Communication Systems, ICSPCS

作者： Trung Q. Duong Hans-Jurgen Zepernick Radio Communications Group Blekinge Institute of Technology Ronneby Sweden

A robust image transmission scheme deploying distributed-Alamouti space-time code (STC) over wireless relay networks is proposed. Specifically, we propose a cross-layer design combining application, networking, and physical layer to enhance the quality of multimedia data over error-prone wireless channels. At the application layer, a coded image sequence based on the wavelet transform is divided into two priorities. The higher priority contains the most significant information of image and the lower priority conveys less important information. At the physical layer, two pre-assigned relays cooperate to form a distributed-Alamouti code which help the source to transmit the low priority sequence of the image over error-prone wireless channels. At the networking layer, among the whole relay nodes in the networks we select two relay nodes which provides the maximum signal-to-noise ratio to convey the higher priority. With this strategy, we implicitly achieve unequal error protection for image transmission without controlling power or varying modulation and coding at the transmitter at the expense of increasing network's overhead (i.e. feedback information). The selection of relays needs limited feedback, hence, enabling our proposed scheme feasible in practical applications. Analytical and simulation results are provided to verify our proposal. It has been shown that the image quality is remarkably enhanced in terms of objective quality using the proposed scheme compared to the conventional transmission schemes.

关键词： Robustness image communication Relays Protective relaying Physical layer Feedback Space time codes Cross layer design image sequences wavelet transforms

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Dual Tree Complex wavelet Transform and Spread Spectrum Technique for Robust Medical image Watermarking

Dual Tree Complex Wavelet Transform and Spread Spectrum Tech...

引用

Intelligent Cybernetics Technology & applications (ICICyTA), International conference on

作者： Ganta Catur Paramitha Ledya Novamizanti Gelar Budiman School of Electrical Engineering Telkom University Bandung Indonesia Human Centric (HUMIC) Engineering Telkom University Bandung Indonesia

The rapid evolution of technology and computer networks has brought about a paradigm shift in data transmission within the medical field, facilitating the utilization of wireless networks for seamless communication. However, this convenience also introduces various challenges, including copyright infringement, data theft, and the imperative need for robust ownership identification during data transmission across open networks. Addressing these concerns, watermarking emerges as a practical solution by discreetly embedding confidential personal information within other data. This paper introduces a watermarking technique designed to enhance the security of medical images, utilizing a fusion of the Dual-Tree Complex wavelet Transform and spread spectrum (DTCWT-SS) methods. The watermark image undergoes a pre-processing stage involving a spread spectrum modulation technique employing a random pseudo-noise code. Subsequently, the watermark series is meticulously embedded into the DTCWT-based host image, resulting in the creation of a watermarked image. At the receiving end, the watermarked image undergoes processing by DTCWT, and the optimal subband is carefully selected to extract the original watermark with high fidelity. The proposed scheme achieves an impressive peak signal-to-noise ratio of over 50 dB for each medical image modality, underscoring the preservation of image quality despite watermarking. Moreover, the watermark retrieval process demonstrates exceptional performance, achieving a normalized correlation of 1 and a bit error rate of 0 under no-attack conditions, ensuring accurate and reliable watermark extraction. Notably, the proposed model exhibits robustness against signal processing attacks and noise addition, confirming its efficacy in various scenarios and resilience against potential threats.

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Double-density dual-tree wavelet-based polarimetry analysis

Double-density dual-tree wavelet-based polarimetry analysis

引用

IEEE National conference on Aerospace and Electronics (NAECON)

作者： Kyle Harrity Soundararajan Ezekiel Adnan Bubalo Erik Blasch Mark Alford Indiana University of Pennsylvania Indiana PA Air Force Research Laboratory Rome NY

For the past two decades, the Discrete wavelet Transformation (DWT) has been successfully applied to many fields. For image processing applications, the DWT can produce non-redundant representations of an input image with greater performance than other wavelet methods. Further, the DWT provides a better spatial and spectral localization of image representation, capable of revealing smaller changes, trends, and breakdown points that classical methods often miss. However, the DWT has its own limitations and disadvantages such as lack of shift invariance. That is, if the input signal or image is shifted, then the wavelet coefficients will exacerbate that shift. The DWT also lacks the ability to represent directional cases. The Double Density Dual-Tree Discrete wavelet Transformation (D3TDWT) is a relatively new and enhanced version of the DWT with two scaling functions and four distinct wavelets designed in such a way that one pair of wavelets is offset with another pair so that the first pair lies in between the second. In this paper, we propose a D3TDWT polarimetry analysis method to analyze Long Wave Infrared (LWIR) polarimetry imagery to discriminate objects such as people and vehicles from background clutter. The D3TDWT method can be applied to a wide range of applications such as change detection, shape extraction, target recognition, and simultaneous tracking and identification.

关键词： Discrete wavelet transforms image segmentation Polarimetry image edge detection Target tracking

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Evaluation of sparsifying algorithms for speech signals

Evaluation of sparsifying algorithms for speech signals

引用

International conference on Computer and Communication Engineering, ICCCE

作者： Liban A. Kassim Othman O. Khalifa T. S. Gunawan Faculty of Engineering International Islamic University Malaysia Kuala Lumpur Malaysia

Sparse representations of signals have been used in many areas of signal and image processing. It has also played an important role in compressive sensing algorithms since it performs well in sparse signals. A sparse representation is one in which small number of coefficients contain large proportion of the energy. Sparsity is important also in speech compression and coding, where the signal can be compressed in pre-processing stages. It leads to efficient and robust methods for compression, detection denoising and signal separation. The objective of this paper is to evaluate several transforms which is used to sparsify the speech signals. Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) and Discrete wavelet Transform (DWT) will be compared and evaluated based on Gini Index. Sparsity properties and measures will be reviewed in this paper. Finally, sparse applications in speech compression and compressive sensing will be discussed.

关键词： Discrete cosine transforms Discrete wavelet transforms Indexes image coding Speech

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Architecture of wavelet packet transform for 1-D signal

Architecture of wavelet packet transform for 1-D signal

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Canadian conference on Electrical and Computer Engineering (CCECE)

作者： S.M. Aroutchelvame K. Raahemifar Department of Electrical & Computer Engineering Ryerson University Toronto ONT Canada

Many signal and image processing applications will be more benefited if the transform gives good spectral and temporal resolution in arbitrary regions of the time-frequency plane that is provided by the discrete wavelet packet transform (DWPT). In this paper, the architecture for lifting scheme based Daubechies 9/7 wavelet is proposed. The proposed architecture performs both forward and inverse transform. The architecture does not require any extra memory/FIFOs to store the intermediate results. The proposed architecture is verified by performing DWPT for images of size 64times64. The architecture has been described in VHDL at the RTL level and simulated successfully using ModelSim simulation environment

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