Proceedings of the Scientific Data Compression Workshop

Continuing advances in space and Earth science requires increasing amounts of data to be gathered from spaceborne sensors. NASA expects to launch sensors during the next two decades which will be capable of producing an aggregate of 1500 Megabits per second if operated simultaneously. Such high data rates cause stresses in all aspects of end-to-end data systems. Technologies and techniques are needed to relieve such stresses. Potential solutions to the massive data rate problems are: data editing, greater transmission bandwidths, higher density and faster media, and data compression. Through four subpanels on Science Payload Operations, Multispectral Imaging, Microwave Remote Sensing and Science Data Management, recommendations were made for research in data compression and scientific data applications to space platforms

Side-Information For Steganography Design And Detection

Today, the most secure steganographic schemes for digital images embed secret messages while minimizing a distortion function that describes the local complexity of the content. Distortion functions are heuristically designed to predict the modeling error, or in other words, how difficult it would be to detect a single change to the original image in any given area. This dissertation investigates how both the design and detection of such content-adaptive schemes can be improved with the use of side-information. We distinguish two types of side-information, public and private: Public side-information is available to the sender and at least in part also to anybody else who can observe the communication. Content complexity is a typical example of public side-information. While it is commonly used for steganography, it can also be used for detection. In this work, we propose a modification to the rich-model style feature sets in both spatial and JPEG domain to inform such feature sets of the content complexity. Private side-information is available only to the sender. The previous use of private side-information in steganography was very successful but limited to steganography in JPEG images. Also, the constructions were based on heuristic with little theoretical foundations. This work tries to remedy this deficiency by introducing a scheme that generalizes the previous approach to an arbitrary domain. We also put forward a theoretical investigation of how to incorporate side-information based on a model of images. Third, we propose to use a novel type of side-information in the form of multiple exposures for JPEG steganography

Schémas de tatouage d'images, schémas de tatouage conjoint à la compression, et schémas de dissimulation de données

In this manuscript we address data-hiding in images and videos. Specifically we address robust watermarking for images, robust watermarking jointly with compression, and finally non robust data-hiding.The first part of the manuscript deals with high-rate robust watermarking. After having briefly recalled the concept of informed watermarking, we study the two major watermarking families : trellis-based watermarking and quantized-based watermarking. We propose, firstly to reduce the computational complexity of the trellis-based watermarking, with a rotation based embedding, and secondly to introduce a trellis-based quantization in a watermarking system based on quantization.The second part of the manuscript addresses the problem of watermarking jointly with a JPEG2000 compression step or an H.264 compression step. The quantization step and the watermarking step are achieved simultaneously, so that these two steps do not fight against each other. Watermarking in JPEG2000 is achieved by using the trellis quantization from the part 2 of the standard. Watermarking in H.264 is performed on the fly, after the quantization stage, choosing the best prediction through the process of rate-distortion optimization. We also propose to integrate a Tardos code to build an application for traitors tracing.The last part of the manuscript describes the different mechanisms of color hiding in a grayscale image. We propose two approaches based on hiding a color palette in its index image. The first approach relies on the optimization of an energetic function to get a decomposition of the color image allowing an easy embedding. The second approach consists in quickly obtaining a color palette of larger size and then in embedding it in a reversible way.Dans ce manuscrit nous abordons l’insertion de données dans les images et les vidéos. Plus particulièrement nous traitons du tatouage robuste dans les images, du tatouage robuste conjointement à la compression et enfin de l’insertion de données (non robuste).La première partie du manuscrit traite du tatouage robuste à haute capacité. Après avoir brièvement rappelé le concept de tatouage informé, nous étudions les deux principales familles de tatouage : le tatouage basé treillis et le tatouage basé quantification. Nous proposons d’une part de réduire la complexité calculatoire du tatouage basé treillis par une approche d’insertion par rotation, ainsi que d’autre part d’introduire une approche par quantification basée treillis au seind’un système de tatouage basé quantification.La deuxième partie du manuscrit aborde la problématique de tatouage conjointement à la phase de compression par JPEG2000 ou par H.264. L’idée consiste à faire en même temps l’étape de quantification et l’étape de tatouage, de sorte que ces deux étapes ne « luttent pas » l’une contre l’autre. Le tatouage au sein de JPEG2000 est effectué en détournant l’utilisation de la quantification basée treillis de la partie 2 du standard. Le tatouage au sein de H.264 est effectué à la volée, après la phase de quantification, en choisissant la meilleure prédiction via le processus d’optimisation débit-distorsion. Nous proposons également d’intégrer un code de Tardos pour construire une application de traçage de traîtres.La dernière partie du manuscrit décrit les différents mécanismes de dissimulation d’une information couleur au sein d’une image en niveaux de gris. Nous proposons deux approches reposant sur la dissimulation d’une palette couleur dans son image d’index. La première approche consiste à modéliser le problème puis à l’optimiser afin d’avoir une bonne décomposition de l’image couleur ainsi qu’une insertion aisée. La seconde approche consiste à obtenir, de manière rapide et sûre, une palette de plus grande dimension puis à l’insérer de manière réversible

A leaky waveguide all-optical analog-to-digital converter

In this thesis we describe a novel all-optical analog-to-digital converter (AOADC) based on a leaky waveguide deflector. The principle of the spatial sampling AOADC is to convert an electrical signal to its corresponding optical deflection angle and then sample and quantize this angle in the spatial domain, instead of the amplitude domain. This AOADC is designed for broadband digital receivers working at frequencies above 20 GHz (a minimum 40 GS/s sampling rate) and provides a resolution higher than 6 bits. An original design based on GRISM (Grating and pRISM) is investigated for a high-resolution ADC implementation; and its challenges have been identified. The investigation provides a general model of spatial sampling AOADCs and highlights their advantages of immunity to optical intensity fluctuation. Later we proposed an AOADC that employs a leaky waveguide structure that is different from any other optical ADC. The AOADC consists of a sampler based on a mode-locked laser and a leaky waveguide deflector driven by traveling wave electrodes, a quantizer based on an integrated optical collector array and broadband photodetectors. These components provide the AOADC with a higher deflection angle and angular resolution resulting in high bit resolution without consuming significant power. The quantization of the deflection angle is done by a simple spatial quantizer that digitizes as well as encodes the signal simultaneously. A detailed design of the E-O deflector and the spatial quantizer has been analyzed and simulated; and some preliminary tests have been conducted. This thesis summarizes our contributions in designing and modeling this novel spatial sampling AOADC.Ph.D., Electrical Engineering -- Drexel University, 200

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

Perceptually lossless coding of medical images - from abstraction to reality

This work explores a novel vision model based coding approach to encode medical images at a perceptually lossless quality, within the framework of the JPEG 2000 coding engine. Perceptually lossless encoding offers the best of both worlds, delivering images free of visual distortions and at the same time providing significantly greater compression ratio gains over its information lossless counterparts. This is achieved through a visual pruning function, embedded with an advanced model of the human visual system to accurately identify and to efficiently remove visually irrelevant/insignificant information. In addition, it maintains bit-stream compliance with the JPEG 2000 coding framework and subsequently is compliant with the Digital Communications in Medicine standard (DICOM). Equally, the pruning function is applicable to other Discrete Wavelet Transform based image coders, e.g., The Set Partitioning in Hierarchical Trees. Further significant coding gains are exploited through an artificial edge segmentatio n algorithm and a novel arithmetic pruning algorithm. The coding effectiveness and qualitative consistency of the algorithm is evaluated through a double-blind subjective assessment with 31 medical experts, performed using a novel 2-staged forced choice assessment that was devised for medical experts, offering the benefits of greater robustness and accuracy in measuring subjective responses. The assessment showed that no differences of statistical significance were perceivable between the original images and the images encoded by the proposed coder

Regime Change: Sampling Rate vs. Bit-Depth in Compressive Sensing

The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by exploiting inherent structure in natural and man-made signals. It has been demonstrated that structured signals can be acquired with just a small number of linear measurements, on the order of the signal complexity. In practice, this enables lower sampling rates that can be more easily achieved by current hardware designs. The primary bottleneck that limits ADC sampling rates is quantization, i.e., higher bit-depths impose lower sampling rates. Thus, the decreased sampling rates of CS ADCs accommodate the otherwise limiting quantizer of conventional ADCs. In this thesis, we consider a different approach to CS ADC by shifting towards lower quantizer bit-depths rather than lower sampling rates. We explore the extreme case where each measurement is quantized to just one bit, representing its sign. We develop a new theoretical framework to analyze this extreme case and develop new algorithms for signal reconstruction from such coarsely quantized measurements. The 1-bit CS framework leads us to scenarios where it may be more appropriate to reduce bit-depth instead of sampling rate. We find that there exist two distinct regimes of operation that correspond to high/low signal-to-noise ratio (SNR). In the measurement compression (MC) regime, a high SNR favors acquiring fewer measurements with more bits per measurement (as in conventional CS); in the quantization compression (QC) regime, a low SNR favors acquiring more measurements with fewer bits per measurement (as in this thesis). A surprise from our analysis and experiments is that in many practical applications it is better to operate in the QC regime, even acquiring as few as 1 bit per measurement. The above philosophy extends further to practical CS ADC system designs. We propose two new CS architectures, one of which takes advantage of the fact that the sampling and quantization operations are performed by two different hardware components. The former can be employed at high rates with minimal costs while the latter cannot. Thus, we develop a system that discretizes in time, performs CS preconditioning techniques, and then quantizes at a low rate

Embedded Machine Learning: Emphasis on Hardware Accelerators and Approximate Computing for Tactile Data Processing

Machine Learning (ML) a subset of Artificial Intelligence (AI) is driving the industrial and technological revolution of the present and future. We envision a world with smart devices that are able to mimic human behavior (sense, process, and act) and perform tasks that at one time we thought could only be carried out by humans. The vision is to achieve such a level of intelligence with affordable, power-efficient, and fast hardware platforms. However, embedding machine learning algorithms in many application domains such as the internet of things (IoT), prostheses, robotics, and wearable devices is an ongoing challenge. A challenge that is controlled by the computational complexity of ML algorithms, the performance/availability of hardware platforms, and the application\u2019s budget (power constraint, real-time operation, etc.). In this dissertation, we focus on the design and implementation of efficient ML algorithms to handle the aforementioned challenges. First, we apply Approximate Computing Techniques (ACTs) to reduce the computational complexity of ML algorithms. Then, we design custom Hardware Accelerators to improve the performance of the implementation within a specified budget. Finally, a tactile data processing application is adopted for the validation of the proposed exact and approximate embedded machine learning accelerators. The dissertation starts with the introduction of the various ML algorithms used for tactile data processing. These algorithms are assessed in terms of their computational complexity and the available hardware platforms which could be used for implementation. Afterward, a survey on the existing approximate computing techniques and hardware accelerators design methodologies is presented. Based on the findings of the survey, an approach for applying algorithmic-level ACTs on machine learning algorithms is provided. Then three novel hardware accelerators are proposed: (1) k-Nearest Neighbor (kNN) based on a selection-based sorter, (2) Tensorial Support Vector Machine (TSVM) based on Shallow Neural Networks, and (3) Hybrid Precision Binary Convolution Neural Network (BCNN). The three accelerators offer a real-time classification with monumental reductions in the hardware resources and power consumption compared to existing implementations targeting the same tactile data processing application on FPGA. Moreover, the approximate accelerators maintain a high classification accuracy with a loss of at most 5%