Внедрение ЦВЗ в аудиосигналы на основе пакетной вейвлет-декомпозиции и частотного маскирования

Рассмотрен спектральный поход к построению систем с цифровыми водяными знаками для аудио-сигналов. Эффективность предложенной слепой схемы создания ЦВЗ основывается на использовании особенностей восприятия звука системой человеческого слуха и анализе влияния на спектральные составляющие сигнала типичных операций обработки.In this work the spectral method for building of systems with the digital audio watermarks is considered. The effectiveness of the proposed blind watermarking scheme is based on features of human auditory system and also on the analysis of typical processing operations influences to the spectral components of audio signal

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

High capacity data embedding schemes for digital media

High capacity image data hiding methods and robust high capacity digital audio watermarking algorithms are studied in this thesis. The main results of this work are the development of novel algorithms with state-of-the-art performance, high capacity and transparency for image data hiding and robustness, high capacity and low distortion for audio watermarking.En esta tesis se estudian y proponen diversos métodos de data hiding de imágenes y watermarking de audio de alta capacidad. Los principales resultados de este trabajo consisten en la publicación de varios algoritmos novedosos con rendimiento a la altura de los mejores métodos del estado del arte, alta capacidad y transparencia, en el caso de data hiding de imágenes, y robustez, alta capacidad y baja distorsión para el watermarking de audio.En aquesta tesi s'estudien i es proposen diversos mètodes de data hiding d'imatges i watermarking d'àudio d'alta capacitat. Els resultats principals d'aquest treball consisteixen en la publicació de diversos algorismes nous amb rendiment a l'alçada dels millors mètodes de l'estat de l'art, alta capacitat i transparència, en el cas de data hiding d'imatges, i robustesa, alta capacitat i baixa distorsió per al watermarking d'àudio.Societat de la informació i el coneixemen

Image Compression using Discrete Cosine Transform & Discrete Wavelet Transform

Image Compression addresses the problem of reducing the amount of data required to represent the digital image. Compression is achieved by the removal of one or more of three basic data redundancies: (1) Coding redundancy, which is present when less than optimal (i.e. the smallest length) code words are used; (2) Interpixel redundancy, which results from correlations between the pixels of an image & (3) psycho visual redundancy which is due to data that is ignored by the human visual system (i.e. visually nonessential information). Huffman codes contain the smallest possible number of code symbols (e.g., bits) per source symbol (e.g., grey level value) subject to the constraint that the source symbols are coded one at a time. So, Huffman coding when combined with technique of reducing the image redundancies using Discrete Cosine Transform (DCT) helps in compressing the image data to a very good extent. The Discrete Cosine Transform (DCT) is an example of transform coding. The current JPEG standard uses the DCT as its basis. The DC relocates the highest energies to the upper left corner of the image. The lesser energy or information is relocated into other areas. The DCT is fast. It can be quickly calculated and is best for images with smooth edges like photos with human subjects. The DCT coefficients are all real numbers unlike the Fourier Transform. The Inverse Discrete Cosine Transform (IDCT) can be used to retrieve the image from its transform representation. The Discrete wavelet transform (DWT) has gained widespread acceptance in signal processing and image compression. Because of their inherent multi-resolution nature, wavelet-coding schemes are especially suitable for applications where scalability and tolerable degradation are important. Recently the JPEG committee has released its new image coding standard, JPEG-2000, which has been based upon DWT

ИНТЕЛЛЕКТУАЛЬНЫЙ числовым программным ДЛЯ MIMD-компьютер

For most scientific and engineering problems simulated on computers the solving of problems of the computational mathematics with approximately given initial data constitutes an intermediate or a final stage. Basic problems of the computational mathematics include the investigating and solving of linear algebraic systems, evaluating of eigenvalues and eigenvectors of matrices, the solving of systems of non-linear equations, numerical integration of initial- value problems for systems of ordinary differential equations.Для більшості наукових та інженерних задач моделювання на ЕОМ рішення задач обчислювальної математики з наближено заданими вихідними даними складає проміжний або остаточний етап. Основні проблеми обчислювальної математики відносяться дослідження і рішення лінійних алгебраїчних систем оцінки власних значень і власних векторів матриць, рішення систем нелінійних рівнянь, чисельного інтегрування початково задач для систем звичайних диференціальних рівнянь.Для большинства научных и инженерных задач моделирования на ЭВМ решение задач вычислительной математики с приближенно заданным исходным данным составляет промежуточный или окончательный этап. Основные проблемы вычислительной математики относятся исследования и решения линейных алгебраических систем оценки собственных значений и собственных векторов матриц, решение систем нелинейных уравнений, численного интегрирования начально задач для систем обыкновенных дифференциальных уравнений

A new approach for improving transparency of audio watermarking.

Chen Benrong.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 125-130).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- What' s Watermarking --- p.1Chapter 1.2 --- "Information Hiding, Steganography, and Watermarking" --- p.3Chapter 1.3 --- History of Watermarking --- p.5Chapter 1.4 --- Importance of Digital Watermarking --- p.8Chapter 1.5 --- Objectives of the Thesis --- p.9Chapter 1.6 --- Thesis Outline --- p.10Chapter 2 --- Applications and Properties of Audio Watermarking --- p.12Chapter 2.1 --- Applications --- p.13Chapter 2.1.1 --- Ownership Identification and Proof --- p.13Chapter 2.1.2 --- Broadcast Monitoring --- p.16Chapter 2.1.3 --- Other Applications --- p.18Chapter 2.2 --- Properties --- p.19Chapter 2.2.1 --- Transparency --- p.20Chapter 2.2.2 --- Robustness --- p.20Chapter 2.2.3 --- Other Properties --- p.21Chapter 3 --- Possible Methods for Audio Watermarking --- p.24Chapter 3.1 --- Overview of Digital Audio Watermarking System --- p.25Chapter 3.2 --- Review of Current Methods --- p.27Chapter 3.2.1 --- Low Bit Coding --- p.27Chapter 3.2.2 --- Phase Coding --- p.28Chapter 3.2.3 --- Echo Coding --- p.29Chapter 3.2.4 --- Spread Spectrum Watermarking --- p.30Chapter 3.3 --- Other Related Approaches --- p.31Chapter 3.4 --- Outline of Proposed New Method --- p.33Chapter 4 --- Audio Watermarking System Based on Spread Spectrum --- p.36Chapter 4.1 --- Introduction --- p.36Chapter 4.2 --- Embedding and Detecting Information Bit --- p.39Chapter 4.2.1 --- General Embedding Process --- p.39Chapter 4.2.2 --- General Detection Process --- p.43Chapter 4.2.3 --- Pseudorandom Bit Sequences (PRBS) --- p.45Chapter 4.3 --- An Optimal Embedding Process --- p.48Chapter 4.3.1 --- Objective Metrics for Embedding Process --- p.48Chapter 4.3.2 --- Content Adaptive Embedding --- p.52Chapter 4.3.3 --- Determination of Frame Length L --- p.57Chapter 4.4 --- Requirement For Transparency Improvement --- p.58Chapter 5 --- Sample and Frame Selection For Transparency Improvement --- p.60Chapter 5.1 --- Introduction --- p.60Chapter 5.2 --- Sample Selection --- p.61Chapter 5.2.1 --- General Sample Selection --- p.62Chapter 5.2.2 --- Objective Evaluation Metrics --- p.65Chapter 5.2.3 --- Sample Selection For Transparency Improvement --- p.66Chapter 5.2.4 --- Theoretical Analysis of Sample Selection --- p.87Chapter 5.3 --- Frame Sclcction --- p.90Chapter 5.3.1 --- General Frame Selection --- p.91Chapter 5.3.2 --- Frame Selection For Transparency Improvement --- p.94Chapter 5.4 --- Watermark Information Retrieve --- p.103Chapter 6 --- Psychoacoustic Model For Robustness Verification --- p.105Chapter 6.1 --- Introduction of Human Auditory System --- p.106Chapter 6.1.1 --- Absolute Hearing Threshold --- p.106Chapter 6.1.2 --- Critical Bands --- p.108Chapter 6.1.3 --- Masking Effect --- p.111Chapter 6.2 --- Psychoacoustic Model of Human Auditory System --- p.112Chapter 6.3 --- Robustness Verification by Psychoacoustic Model Analysis --- p.117Chapter 7 --- Conclusions and Suggestions For Future Research --- p.121Chapter 7.1 --- Conclusions --- p.121Chapter 7.2 --- Suggestions For Future Research --- p.123Bibliography --- p.12

Efficiency in audio processing : filter banks and transcoding

Audio transcoding is the conversion of digital audio from one compressed form A to another compressed form B, where A and B have different compression properties, such as a different bit-rate, sampling frequency or compression method. This is typically achieved by decoding A to an intermediate uncompressed form, and then encoding it to B. A significant portion of the involved computational effort pertains to operating the synthesis filter bank, which is an important processing block in the decoding stage, and the analysis filter bank, which is an important processing block in the encoding stage. This thesis presents methods for efficient implementations of filter banks and audio transcoders, and is separated into two main parts. In the first part, a new class of Frequency Response Masking (FRM) filter banks is introduced. These filter banks are usually characterized by comprising a tree-structured cascade of subfilters, which have small individual filter lengths. Methods of complexity reduction are proposed for the scenarios when the filter banks are operated in single-rate mode, and when they are operated in multirate mode; and for the scenarios when the input signal is real-valued, and when it is complex-valued. An efficient variable bandwidth FRM filter bank is designed by using signed-powers-of-two reduction of its subfilter coefficients. Our design has a complexity an order lower than that of an octave filter bank with the same specifications. In the second part, the audio transcoding process is analyzed. Audio transcoding is modeled as a cascaded quantization process, and the cascaded quantization of an input signal is analyzed under different conditions, for the MPEG 1 Layer 2 and MP3 compression methods. One condition is the input-to-output delay of the transcoder, which is known to have an impact on the audio quality of the transcoded material. Methods to reduce the error in a cascaded quantization process are also proposed. An ultra-fast MP3 transcoder that requires only integer operations is proposed and implemented in software. Our implementation shows an improvement by a factor of 5 to 16 over other best known transcoders in terms of execution speed

Methods of covert communication of speech signals based on a bio-inspired principle

This work presents two speech hiding methods based on a bio-inspired concept known as the ability of adaptation of speech signals. A cryptographic model uses the adaptation to transform a secret message to a non-sensitive target speech signal, and then, the scrambled speech signal is an intelligible signal. The residual intelligibility is extremely low and it is appropriate to transmit secure speech signals. On the other hand, in a steganographic model, the adapted speech signal is hidden into a host signal by using indirect substitution or direct substitution. In the first case, the scheme is known as Efficient Wavelet Masking (EWM), and in the second case, it is known as improved-EWM (iEWM). While EWM demonstrated to be highly statistical transparent, the second one, iEWM, demonstrated to be highly robust against signal manipulations. Finally, with the purpose to transmit secure speech signals in real-time operation, a hardware-based scheme is proposedEsta tesis presenta dos métodos de comunicación encubierta de señales de voz utilizando un concepto bio-inspirado, conocido como la “habilidad de adaptación de señales de voz”. El modelo de criptografía utiliza la adaptación para transformar un mensaje secreto a una señal de voz no confidencial, obteniendo una señal de voz encriptada legible. Este método es apropiado para transmitir señales de voz seguras porque en la señal encriptada no quedan rastros del mensaje secreto original. En el caso de esteganografía, la señal de voz adaptada se oculta en una señal de voz huésped, utilizando sustitución directa o indirecta. En el primer caso el esquema se denomina EWM y en el segundo caso iEWM. EWM demostró ser altamente transparente, mientras que iEWM demostró ser altamente robusto contra manipulaciones de señal. Finalmente, con el propósito de transmitir señales de voz seguras en tiempo real, se propone un esquema para dispositivos hardware

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity