This paper describes state-of-the-art approaches to near-lossless image compression by adaptive causal DPCM and presents two advanced schemes based on crisp and fuzzy switching of predictors, respectively. The former relies on a linear-regression prediction in which a different predictor is employed for each image block. Such block-representative predictors are calculated from the original data set through an iterative relaxation-labeling procedure. Coding time are affordable thanks to fast convergence of training. Decoding is always performed in real time. The latter is still based on adaptive MMSE prediction in which a different predictor at each pixel position is achieved by blending a number of prototype predictors through adaptive weights calculated from the past decoded samples. Quantization error feedback loops are introduced into the basic lossless encoders to enable user-defined upper-bounded reconstruction errors. Both schemes exploit context modeling of prediction errors followed by arithmetic coding to enhance entropy coding performances. A thorough performance comparison on a wide test image set show the superiority of the proposed schemes over both up-to-date encoders in the literature and new/upcoming standards.

Image compression frequently supports reduced storage requirement in a computer system, as well as enhancement of effective channel bandwidth in a communication system, by decreasing the source bit rate through reduction of source redundancy. The majority of image compression techniques emphasize pixel-level operations, such as matching rectangular or elliptical sampling blocks taken from the source data stream, with exemplars stored in a database (e.g., a codebook in vector quantization or VQ). Alternatively, one can represent a source block via transformation, coefficient quantization, and selection of coefficients deemed significant for source content approximation in the decompressed image. This approach, called transform coding (TC), has predominated for several decades in the signal and image processing communities. A further technique that has been employed is the deduction of affine relationships from source properties such as local self-similarity, which supports the construction of adaptive codebooks in a self-VQ paradigm that has been called iterated function systems (IFS). Although VQ, TC, and IFS based compression algorithms have enjoyed varying levels of success for different types of applications, bit rate requirements, and image quality constraints, few of these algorithms examine the higher-level spatial structure of an image, and fewer still exploit this structure to enhance compression ratio. In this paper, we discuss a fourth type of compression algorithm, called object-based compression, which is based on research in joint segmentaton and compression, as well as previous research in the extraction of sketch-like representations from digital imagery. Here, large image regions that correspond to contiguous recognizeable objects or parts of objects are segmented from the source, then represented compactly in the compressed image. Segmentation is facilitated by source properties such as size, shape, texture, statistical properties, and spectral signature. In particular, discussion addresses issues such as efficient boundary representation, variance assessment and representation, as well as a texture classification and replacement algorithms that can decrease compression overhead and increase reconstruction fidelity in the decompressed image. Contextual extraction of motion patterns in digital video sequences, using a frequency-domain pattern recognition technique based on interframe correlation, is described in a companion paper. This technique can also be extended to multidimensional image domains, to support joint spectral, spatial, and temporal compression.

The advent of the internet has opened a host of new and exciting questions in the science and mathematics of information organization and data mining. In particular, a highly ambitious promise of the internet is to bring the bulk of human knowledge to everyone with access to a computer network, providing a democratic medium for sharing and communicating knowledge regardless of the language of the communication. The development of sharing and communication of knowledge via transfer of digital files is the first crucial achievement in this direction. Nonetheless, available solutions to numerous ancillary problems remain far from satisfactory. Among such outstanding problems are the first few fundamental questions that have been responsible for the emergence and rapid growth of the new field of Knowledge Engineering, namely, classification of forms of data, their effective organization, and extraction of knowledge from massive distributed data sets, and the design of fast effective search engines. The precision of machine learning algorithms in classification and recognition of image data (e.g. those scanned from books and other printed documents) are still far from human performance and speed in similar tasks. Discriminating the many forms of ASCII data from each other is not as difficult in view of the emerging universal standards for file-format. Nonetheless, most of the past and relatively recent human knowledge is yet to be transformed and saved in such machine readable formats. In particular, an outstanding problem in knowledge engineering is the problem of organization and management--with precision comparable to human performance--of knowledge in the form of images of documents that broadly belong to either text, image or a blend of both. It was shown in that the effectiveness of OCR was intertwined with the success of language and font recognition.

A novel rate-distortion optimization algorithm for JPEG 2000 is proposed. This algorithm meets memory buffer requirement for the compressed bit streams quite strictly according to a given bit rate. Moreover, before the encoding process even starts, a required memory buffer size can be estimated. This algorithm can also help avoid unnecessary encoding for some parts of an image. In this sense, it is memory efficient and performs progressive encoding. While a rate-distortion optimization algorithm is generally applied after complete encoding procedures for images in JPEG 2000, the proposed algorithm can save both memory requirement and encoding time significantly at low bit rate by avoiding complete encoding.

In this paper, we introduce vector-lifting schemes which allow to generate very compact multiresolution representations, suitable for lossless and progressive coding of multispectral images. These new decomposition schemes exploit simultaneously the spatial and the spectral redundancies contained in multispectral images. When the spectral bands have different dynamic ranges, we improve dramatically the performances of the proposed schemes by a reversible histogram modification based on sorting permutations. Simulation tests carried out on real images allow to evaluate the performances of this new compression method. They indicate that the achieved compression ratios are higher than those obtained with currently used lossless coders.

The privacy and data integrity are not guaranteed in current wireless communications due to the security hole inside the Wireless Application Protocol (WAP) version 1.2 gateway. One of the remedies is to provide an end-to-end security in m-commerce by applying application level security on top of current WAP1.2. The traditional security technologies like RSA and ECC applied on enterprise's server are not practical for wireless devices because wireless devices have relatively weak computation power and limited memory compared with server. In this paper, we developed a lattice based polynomial digital signature system based on NTRU's Polynomial Authentication and Signature Scheme (PASS), which enabled the feasibility of applying high-level security on both server and wireless device sides.

A client-server application for MPEG-4 video distribution, in a VOD (Video-On-Demand) infrastructure, has been built up granting authorized fruition by means of digital watermarking. Once the consumer has chosen to watch a program, his smart card code, plugged in the set top box, is sent back to the server side. This code will be embedded, exactly at that time, in the video sequence before streaming it towards the client through the network (the code will be obviously used for payment too). The client side, adequately equipped with the watermark detector, receives the video and checks it by extracting the identifying code, this one is matched with the code located in the end user smart card and if the comparison is right rendering is allowed, otherwise decoding is stopped and fruition is inhibited.

In this paper, a new encryption mode, which we call the 2D-Encryption Mode, is presented. 2D-Encrytpion Mode extends 1D-encryption modes, as ECB, CBC and CTR, to 2 dimensions. It has good security and practical properties. We first look at the type of problems it tries to solve, then describe the technique and its properties, and present a detailed mathematical analysis of its security, and finally discuss some practical issues related to its implementation.

In this paper, we propose a robust image-in-image watermarking algorithm based on the fast Hadamard transform (FHT) for the copyright protection of digital images. Most current research makes use of a normally distributed random vector as a watermark and where the watermark can only be detected by cross-correlating the received coefficients with the watermark generated by secret key and then comparing an experimental threshold value. However, the FHT image-in-image method involves a "blind" watermarking process that retrieves the watermark without the need for an original image present. In the proposed approach, a number of pseudorandom selected 8×8 sub-blocks of original image and a watermark image are decomposed into Hadamard coefficients. To increase the invisibility of the watermark, a visual model based on original image characteristics, such as edges and textures are incorporated to determine the watermarking strength factor. All the AC Hadamard coefficients of watermark image is scaled by the watermarking strength factor and inserted into several middle and high frequency AC components of the Hadamard coefficients from the sub-blocks of original image. To further increase the reliability of the watermarking against the common geometric distortions, such as rotation and scaling, a post-processing technique is proposed. Understanding the type of distortion provides a mean to apply a reversal of the attack on the watermarked image, enabling the restoration to the synchronization of the embedding positions. The performance of the proposed algorithm is evaluated using Stirmark. The experiment uses container image of size 512×512×8bits and the watermark image of size 64×64×8bits. It survives about 60% of all Stirmark attacks. The simplicity of Hadamard transform offers a significant advantage in shorter processing time and ease of hardware implementation than the commonly used DCT and DWT techniques.

In this paper we propose to employ entropy-constrained predictive coding for lossy compression of SAR raw data. We exploit the known result that a blockwise normalized SAR raw signal is a Gaussian stationary process in order to design an optimal decorrelator for this signal. The proposed predictive coding algorithm performs entropy-constrained quantization of the prediction error, followed by entropy coding; the algorithm exhibits a number of advantages, and notably a very high performance gain, with respect to other techniques such as FBAQ or methods based on transform coding. Simulation results on real-world SIR-C/X-SAR as well as simulated raw and image data show that the proposed algorithm significantly outperforms FBAQ as to SNR, at a computational cost compatible with modern SAR systems.

This work reports about a specific application concerning lossy compression of multispectral (MS) and panchromatic (P) images collected by spaceborne platforms. Generally, the former is a set of narrow-band spectral images, while the latter is a single broad-band observation imaged in the visible and near-infrared wavelengths. Since high resolution spectral observations having high SNR are difficult to obtain, and especially to transmit, the P image, having resolution typically four times that of MS, but slightly lower SNR, is added to the MS data and used with the main purpose of expediting both visual and automatic identification tasks, possibly through an integration (merge) with the lower resolution MS data. Whenever MS data at the same resolution of the P data and with adequate SNR were hypotetically available on board, the bottleneck of downlink to receiving stations would impose severe limitations in the bit rate, so that a lossy compression would be mandatory. The consequence of the loss of information is a distortion, both radiometric and especially spectral, which may be easily quantified. Experimental results were carried out on simulated SPOT 5 data, constituted by three 10m MS bands (XS) and one 2.5m P band, all 8 b/pel, of a highly detailed urban area. The main result is that, for cumulative bit rates larger than 4.2 b/pel (at a 2.5m scale), lossless compression of the three 10m XS bands and near-lossless compression of the 2.5m P band, followed by XS + P fusion of the data decoded at the receiving station, is preferable, in terms of radiometric and especially of spectral distortion, to lossy compression of the three 2.5m XS bands, even if data with such a resolution were hypotetically available on board.

The statistical dependency across levels (or scales) in the wavelet transform of natural images can be effectively exploited to design state-of-the-art image coders. This paper presents a comparative evaluation of three such algorithms, although only one of these algorithms is detailed to a larger extend. The first method explicitly models the conditional statistics of the wavelet coefficients for bit-plane encoding. The second method performs an implicit sorting of the coefficients by sending zerotree symbols. Both methods produce an embedded code, i.e. the optimal image at any rate can be identified with the truncated bitstream of the highest possible rate, but the second algorithm does not depend as much as the first on the use of arithmetic coding. The third method is based on lattice vector quantization: statistical dependency within the wavelet transform is taken into account by conditioning the encoding of the vector norm on the value of a predictor. All three algorithms yield approximately comparable experimental results for different corpuses of images. This supports our view that the essence of high performance image compression is a careful modeling of the conditional image statistics.

The specification and the management of Quality of Service (QoS) are important aspects in mobile telecommunication networks. There are increasing interests and open issues in providing users with a wide range of multimedia services, in particular for video-telephony applications in a third generation cellular system environment. This paper presents a performance assessment of combined voice-video transmission in a realistic UMTS scenario in which the radio interface is simulated by using the environment offered by OPNET (Optimized Network Engineering Tool). In such a context, it is possible to take into account the effects of interference and mobility in a classical cellular scenario. Hence, we can evaluate the effectiveness of different solutions: power control and macro-diversity at the physical layer, coding and retransmission strategies at the link layer and handover, resource allocation and scheduling schemes at the network layer. Power control is designed in order to adapt the power transmitted by mobile terminals on the basis of interference level, traffic and channel conditions, and required packet loss probability. Two main aspects are considered to test the system performance: the ability to support given QoS requirements for both voice and video services - by using accurate models for voice sources and real H.263 video sources - and the effectiveness in the utilization of system resources. Toward this end, simulations are performed for various configurations of parameters and different environmental conditions with a particular attention devoted to the QoS for video transmission.

Until now, it is still difficult to identify different kinds of celestial bodies depending on their spectra, because it needs a lot of astronomers’ manual work of measuring, marking and identifying, which is generally very hard and time-consuming. And with the exploding spectral data from all kinds of telescopes, it is becoming more and more urgent to find a thoroughly automatic way to deal with such a kind of problem. In fact, when we change our viewpoint, we can find that it is a traditional problem in pattern recognition field when considering the whole process of dealing with spectral signals: filtering noises, extracting features, constructing classifiers, etc. The main purpose for automatic classification and recognition of spectra in LAMOST (Large Sky Area Multi-Object Fibre Spectroscopic Telescope) project is to identify a celestial body’s type only based on its spectrum. For this purpose, one of the key steps is to establish a good model to describe all kinds of spectra and thus it will be available to construct some excellent classifiers. In this paper, we present a novel describing language to represent spectra. And then, based on the language, we use some algorithms to extract classifying rules from raw spectra datasets and then construct classifiers to identify spectra by using rough set method. Compared with other methods, our technique is more similar to man’s thinking way, and to some extent, efficient.

We consider the scenario of blind information hiding in images as a communications channel, where the channel noise is caused by the embedding and blind extraction method as well as by any lossy compression method utilized to store and transmit the image. We assume that the objectives of the information hiding method are the maximization of payload and its visual and statistical imperceptibility; also, we assume that the warden is passive. For the specific method of Spread Spectrum Image Steganography (SSIS) we show that the channel can be modeled as a Laplacian distribution, and use this to estimate the channel SNR to be expected for any given signal embedding strength by applying the technique to a range of typical images. Finally, we model the effects of various signal extraction methods and lossy compression. This allows a fair comparison with respect to payload capacity. The results shown in this paper are useful for maximizing the channel usage.

Information hiding can be performed under the guise of a digital image. We consider the following scenario: Alice and Bob share an image and would like to use it as a cover image to communicate a message m. We are interested in answering two questions: What is the maximum amount of information that can be sent for a given level of degradation to an image? and How can this level of efficiency be achieved in practice? We require the recovered message to be the same as the embedded one. Our model begins with Alice compressing a message to obtain a binary sequence with uniform distribution. She then converts the binary sequence into a Q-ary sequence having a pre-defined distribution, and finally adding each symbol to a pixel. The distribution of the Q-ary sequence is chosen such that the amount of information is maximized for a given value of the signal to noise ratio. Bob recovers the sequence by subtracting the image data, and then converting the Q-ary string into the original binary string. We determine the optimal distribution analytically and provide a graphical representation of the variation of the amount of information with signal-to-noise ratio when the size of the alphabet, Q, varies.

In this paper, a character-embedded watermarking algorithm is proposed for copyright protection of satellite images based on the Fast Hadamard transform (FHT). By using a private-key watermarking scheme, the watermark can be retrieved without using the original image. To increase the invisibility of the watermark, a visual model based on original image characteristics, such as edges and textures are incorporated to determine the watermarking strength factor. This factor determines the strength of watermark bits embedded according to the region complexity of the image. Detailed or coarse areas will be assigned more strength and smooth areas with less strength. Error correction coding is also used to increase the reliability of the information bits. A post-processing technique based on log-polar mapping is incorporated to enhance the robustness against geometric distortion attacks. Experiments showed that the proposed watermarking scheme was able to survive more than 70% of attacks from a common benchmarking tool called Stirmark, and about 90% against Checkmark non-geometric attacks. These attacks were performed on a number of SPOT images of size 512×512×8bit embedded with 32 characters. The proposed FHT algorithm also has the advantage of easy software and hardware implementation as well as speed, comparing to other orthogonal transforms such as Cosine, Fourier and wavelet transform.

Interferograms obtained from Fourier transform spectrometers (FTS) can be used to obtain high resolution optical spectral measurements. Accompanying high resolution spectra is the resulting large data volumes. This paper describes efforts directed at defining a predictor that will result in efficient lossless coding of interferograms. Interferograms generally contain informational redundancies that can be exploited by lossless coders. In addition, many interferograms show regions exhibiting short term periodicity. The short term periodicity is derived by taking the Fourier transform of the points preceding the desired prediction point. Prediction residuals are then losslessly coded with the Rice entropy coder. Compared with interferograms compressed with the Rice coding algorithm, the proposed predictor shows improved compression in most cases.

Previously a method has been proposed for a high-performance compression method designed expressly for compressing intercepted radar pulse trains for the purpose of locating the transmitter. The method relies on gating pulses, putting them into a pulse matrix and then using the singular value decomposition (SVD) to compress the signal data. This paper reformulates the pulse gating method and shows that it requires the solution of an integer linear programming problem and several standard methods are first considered. It is shown that the large number of constraints in the original formulation can be significantly reduced by replacing the constraint set by its convex hull; simple rules for identifying the convex hull are given. However, even with these reductions the execution time for these methods can be prohibitive at very large pulse counts; furthermore, these methods exhibited numerical precision and convergence problems as the number of pulses increased. Therefore, an efficient non-standard method for solving this integer optimization problem is developed by exploiting characteristics of the objective function. This method solves the pulse gating problem with short execution times that grow negligibly with increasing pulse counts.

The location of an emitter is estimated by intercepting its signal and sharing the data among several platforms to measure the time-difference-of-arrival (TDOA) and the frequency-difference-of-arrival (FDOA). A common compression approach is to use a rate-distortion criterion where distortion is taken to be the mean-square error (MSE) between the original and compressed versions of the signal. However, we show that this MSE-only approach is inappropriate for TDOA/FDOA estimation and then define a more appropriate, non-MSE distortion measure. This measure is based on the fact that in addition to the dependence on MSE, the TDOA accuracy also depends inversely on the signal's RMS (or Gabor) bandwidth and the FDOA accuracy also depends inversely on the signal's RMS (or Gabor) duration. The form of this new measure must be optimized under the constraint of a specified budget on the total number of bits available for coding. We show that this optimization requires a selection of DFT cells to retain that must be jointly chosen with an appropriate allocation of bits to the selected DFT cells. This joint selection/allocation is a challenging integer optimization problem that still has not been solved. However, we consider three possible sub-optimal approaches and compare their performance.

This paper deals with the efficient storage and transmission of stereo images. It introduces a compression algorithm based on the Discrete Wavelet Transform and an adapted SPIHT-coder. By using objective quality tests, it is shown that this coder is superior to all other coders published so far.

In this paper, a joint image compression and indexing technique using wavelet transform is presented. For compression, scalar quantization and classified vector quantization are applied in the wavelet domain to remove redundancies from different sub-bands according to their distinct characteristics. For indexing, two statistical feature vectors are constructed directly from compression outputs (quantized sub-band data before entropy coding), which facilitate a hierarchical (coarser to finer) indexing procedure and achieve image indexing in the compressed domain. Experimental results show that the joint technique performs with equal effectiveness as either compression or indexing standing alone, while the computational cost for decompression is greatly reduced (only entropy decoding is needed). Thus, the advantages of this joint (dual) image compression-indexing technique and its feasibility for online distributed image retrieval in the arena of exploding networked image applications are demonstrated.

The ongoing, dramatic increase in speed and accessibility in digital computing has been potentiated since the early 1970s by the increasing capacity of integrated circuits expressed as gates per unit chip area. The governing model, called Moore's Law, states that gate density doubles approximately every 18 months, and has held since the early 1970s. However, there exist physical limits on the size of integrated circuit features such as insulators. These limits are expected to be reached in conventional circuit technology in the next 15 to 20 years. At that time, (a) circuit density will no longer increase significantly, or (b) new technologies (if available) are envisioned to support further progress approximating the curve dictated by Moore's Law. Such technological innovations can be classified as: (1) fundamental advances in circuit technology that support further growth in gate density according to Moore’s Law, or (2) technologies that advance state-of-the-art in computing in either hardware or software to provide a constant offset to the graph of Moore’s Law. Examples of the former include quantum computing, and of the latter, more efficient chip layouts. In this paper, we show that another, recently-developed technology exists that can partially ameliorate the performance constraints imposed by physical limits on gate density, thereby providing a sustainable but constant offset of two to three orders of magnitude in Moore’s performance model. Called compressive processing, this technique involves computation using compressed data (e.g., signals or imagery) in compressed form, without decompression, primarily for image and signal processing (ISP) applications. Given a source image a and a compression transform T, a compressed image c = T(a) is subjected to an operation O' that is an analogue over range(T) of an operation on uncompressed imagery b = O(a), such that d = O’(c) = T(O (a)). By applying the inverse of T (denoted by T^-1) to d, we obtain b. If T does not have an inverse, then an approximation T^* to the inverse can be applied, such that the decompressed image is given by d = T^*( O (T(a))) ≈ b, which is the customary case in lossy compression. Analysis emphasizes implementational concerns such as applicable compression transforms (T) and theory that relates compressive operations (O’) to corresponding image or signal processing operations (O). Discussion also includes error propagation in cascades of compressive operations. Performance results are given for various image processing operations.

We propose a unique mathematical framework to deblur, denoise and compress natural images. Images are decomposed in a wavelet packet basis adapted both to the deblurring filter and to the denoising process. Effective denoising is performed by thresholding small wavelet packet coefficients while deblurring is obtained by multiplying the coefficients with a deconvolution kernel. This representation is compressed by quantizing the remaining coefficients and by coding the values using a context-based entropy coder. We present examples of such treatments on a satellite image chain. The results show a significant improvement compared to separate treatments with up-to-date compression approach.

The movement of objects in video sequences comprises a type of spatiotemporal redundancy that can be decreased mathematically to facilitate video compression. This observation holds particularly in the case of periodic motion, for example, bipedal or quadrupedal locomotion or repetitive gestures. Previously-published motion detection techniques were based on optical flow, interframe differences represented in terms of transform coefficient perturbations, or changes in eigenvalues between frames in a video sequence. However, such methods have deficits that include sensitivity to noise, burdensome computational requirements (e.g., floating point operations), and prohibitive instability in the presence of spatial or temporal interframe discontinuities. In this paper, we discuss several techniques of motion detection in two-dimensional images of three-dimensional scenes — pointwise tracking of constant-intensity pixels, region-based vector field characterization of apparent motion, and correlationbased detection. In the latter category is a technique called Interframe Similarity Matrices (ISMs). ISMs were developed and successfully applied by Yacoob, Black, and Davis to address the challenging problem of detecting human and animal motion in surveillance video sequences. In particular, given an N-frame video sequence, an NxN-element interframe correlation matrix can be constructed and Fourier-transformed to obtain an N/2-element power spectrum of interframe periodicities. Different actions (e.g., walking vs. running) and various actors (e.g., quadruped versus human) tend to be characterized by distinct spatiotemporal spectra, and can often be distinguished from one another. Since each spectrum can be computed from a sequence of small image regions, it is possible to represent interframe motion by a pixel tagging technique, thus implementing detection, segmentation, and representation. If there are K objects with M pixels per frame having B bits per pixel (bpp) in N frames of a compressed video sequence, and each object is segmented into a region represented by a P bit tag, then increased compression results if NKMB < NKP, i.e., MB < P. Implementational discussion concerns efficient algorithms for tagging of motion-containing regions, to decrease the representational overhead to several bits per region. We also discuss motion encoding in a compressed format for purposes of efficient extraction of motion parameters from a compressed image, which can support efficient object recognition in highresolution compressed image sequences.

Most digital watermarking algorithms are not robust against geometric attacks. In this paper several improvements of watermarking algorithms are proposed. It may present additional advantages in terms of detection and recovery from geometric attacks by exploiting chaotic spread spectrum and synchronization techniques. These improvements come from three aspects. First, based on the randomness, noise like and extreme sensitivity to initial conditions of chaotic signal, chaotic sequence is exploited as spread spectrum code in the watermarking algorithms. The code spread from chaotic sequence has better performance than pseudo random code because it is massive, arbitrary length and high security. The robustness of the watermarking is improved greatly due to the original watermarking signal is modulated in broadband chaotic signal. Second, a synchronization code generated from chaotic sequence is inserted into the watermarking signal at specified intervals. A watermark cannot be detected correctly after geometric attacks because the synchronization information is lost in watermarking extracting. By locating the synchronization code, re-synchronization can be achieved. Hence, the watermark can be recovered furthest from geometric attacks. Third, the extracted watermark will be post processed with noise reduction filter in order to improve the accuracy of watermarking verification. Based on the characteristics of chaotic sequence, the noise signal can be separated from chaotic background by exploiting wavelet multi-scaling decomposition method. These improvements, which take full advantage of the characteristic of chaotic sequences, are feasible to resist geometric attacks. Experiment results demonstrate its effectiveness.

In this paper, a scheme which following the second generation watermarking paradigm is proposed. The goal of this proposed scheme is basically to increase the robustness against geometric attacks. The host image is decomposed with the wavelet packet. The bit stream of binary watermark is coded into several patterns with salient feature. The circular feature is used in this paper, because: (a) the computational complexity of the method grows rapidly with more complex shapes. (b) circle is rotation-invariant and partially scale-invariant. (c) circular feature can be detected by Hough transform effectively. These patterns are embedded into wavelet packet coefficients according to human perceptual characteristics. A new HVS mask based on wavelet transform is proposed with consideration of local texture characteristics. The introduction of HVS characteristics boosts the performance of the whole scheme. For the detection of watermark, the geometric distortion is calibrated for the contaminated watermarked image. Hough transform is used to detect the circular features in the WP Coefficients. This scheme has the following characteristics: (a) robustness against the common geometric attacks(rotation, scaling, cropping, and etc) is improved significantly. (b) human perceptual characteristics is taken into consideration, so the tradeoff between invisibility and robustness is improved. Results of extensive experiments indicate that this proposed scheme is significantly effective in resisting various geometric attacks such as rotation, scaling, JPEG compression, adding noise, etc.

Multiple digital watermarking technique can resolve the problems of multiple copyright claim and keep the traces of digital products in the different phase of publishing, selling and using. In this paper, a multiple digital watermarking algorithm based on chaotic sequences is proposed. The chaotic sequences have the advantages of massive, high security, and weakest correlation. The massive and independent digital watermark signals are generated through 1-D chaotic maps, which are determined by different initial conditions and parameters. The chaotic digital watermark signals effectively resolve the construction of massive watermarks with good performance. The capacity of the multiple watermarking is also analyzed in this paper. The length of the watermark can be selected adaptively according to the number of the watermarks. Multiple digital watermarking algorithm is more complex than the single watermarking algorithm in the embedding method. The principal problem is how to ensure that the late-coming watermark will not damage the embedded watermarks. Each watermark is added to the middle frequency coefficients of wavelet domain randomly by exploiting 2-D chaotic system, so the embedding and extracting of each watermark does not disturbed each other. Thinking of the parameters of 2-D chaotic system as the key to embedding procedure can prevent the watermarks to be removed malevolently, therefore the performance of security is better. The embedding algorithm based on noise analysis and wavelet transform is also exploited in this paper. To meet the transparence and robustness of the watermark, the watermark strength is adapted to the noise strength within the tolerance of wavelet coefficients. The experimental results demonstrate that this proposed algorithm is robust to many common attacks and the performance can satisfy the requirements in the application.

The suggested device includes a spatial light modulator (SLM) allowing varying the polarization state of an input illumination source. Attached to the SLM a sub wavelength grating is placed per each pixel of the device. This grating is used to encode the input information. The decoding is done by another sub wavelength grating. When the two gratings are attached a variation of the energetic efficiency between the two polarization states occurs. The polarization ratio encrypts the spatial information stream. The sub wavelength grating are hard to copy and thus high encryption capability is obtained.

Segmentation of images to bit planes is one of the techniques for scalable image compression. Assuming our source image to be from a uniform quantizer, we categorize its bit planes on the basis of their significance, into two groups called MSB-planes and LSB-planes. The MSB planes contain low-entropy structural information, whereas LSB planes contain high entropy texture information. Due to the different nature of information and entropy of the two groups, they can be coded and reconstructed by different algorithms. The structural nature of MSB planes, make them more compressible at entropy coding stage, whereas the low significance of LSB-planes can be exploited against their high entropy. We realize the later by subsampling of LSB-planes, which in turn requires attention to the close coupling of MSB and LSB planes at the reconstruction stage. We introduce an estimation algorithm for the LSB planes. The reconstructed images are found to be perceptually comparable to the original images. Quantitative comparison also shows significant coding gain in terms of SNR vs. bit rate.

A novel technique called reverse circuit execution is presented as an efficient approach towards integrated parallel execution of multiple sequential circuit simulators. The unique aspect of this approach is that it does not require source code modifications to either simulation engines or circuit models, and hence holds appeal in situations where parallelism is desirable but without access to simulator and/or model source code (as in the case of commercial simulators with proprietary code concerns). First, algorithms and methodology are presented for transforming an input circuit into another equivalent circuit that is capable of both forward and reverse execution. Following that, it is shown how the transformed circuit can be used towards optimistic synchronization of multiple circuit simulators. As an end result of using our approach, it will be possible to efficiently co-simulate logic circuits partitioned across multiple commercial simulators, by synchronizing their execution using optimistic concurrency protocols.

A scheme for lossy hyperspectral data cube compression, using a linear mixing model approach and wavelet transform, is presented. The data is first compressed in the spectral dimension by using the linear mixing model approximation to reduce the number of dimensions needed to represent the data. The reduced data is then compressed along the spatial dimensions using a wavelet transform. Five hyperspectral data cubes have been tested using the algorithm. Compression ratios of up to 1000:1 are achieved with peak signal-to-noise (PSNR) ratios of over 40 dB. For all test cases, we were able to achieve ratios of over 200:1 with PSNR exceeding 46 dB. The ultra-high compression ratio with low distortion is an improvement over other results reported in the literature. In addition, the reconstructed spectra from the highly compressed file are shown to preserve the overall shape of the original spectra. However, in some cases the curves are slightly offset in some spectral regions from the original.

Electronic states inside heterostructure devices are accurately calculated by using an efficient scheme. The scheme is based on self-consistent solution of Schroedinger’s equation (using variational technique) together with Poisson’s equation. The model is applied to in one dimensional heterostructure FET to determine its characteristics. The model is then extended in two-dimensions to determine the electronic states in low-dimensional heterostructure devices and quantum wires. The advantages and limitations of the present scheme are finally discussed.

A rigorous two-dimensional physical device simulator is developed to characterize the operation of ultra-small and highly doped semiconductor devices. To allow a better understanding of the device behavior, the physical phenomena which are taking place inside these devices are accurately modeled and included in the simulator. Implicit and explicit numerical schemes are applied to self-consistently solve the model equations and the effects of both schemes on the computational efficiency of the simulator are discussed. Finally, the obtained results from the simulator are compared with experimental characteristics of a device with similar structure.