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Color filter array is a spatial multiplexing of pixel-sized filters fabricated over pixel sensors in most color image sensors. The state-of-the-art lossless coding techniques of raw sensor data captured by such sensors leverage spatial or cross-color correlation using lifting schemes. In this paper, we propose a lifting-based lossless white balance algorithm. When applied to the raw sensor data, the spatial bandwidth of the implied chrominance signals decreases. We propose to use this white balance as a pre-processing step to lossless CFA subsampled image/video compression, improving the overall coding efficiency of the raw sensor data. [ABSTRACT FROM AUTHOR]

Lifting-based wavelet transform has been extensively used for efficient compression of various types of visual data. Generally, the performance of such coding schemes strongly depends on the lifting operators used, namely the prediction and update filters. Unlike conventional schemes based on linear filters, we propose, in this paper, to learn these operators by exploiting neural networks. More precisely, a classical Fully Connected Neural Network (FCNN) architecture is firstly employed to perform the prediction and update. Then, we propose to improve this FCNN-based Lifting Scheme (LS) in order to better take into account the input image to be encoded. Thus, a novel dynamical FCNN model is developed, making the learning process adaptive to the input image contents for which two adaptive learning techniques are proposed. While the first one resorts to an iterative algorithm where the computation of two kinds of variables is performed in an alternating manner, the second learning method aims to learn the model parameters directly through a reformulation of the loss function. Experimental results carried out on various test images show the benefits of the proposed approaches in the context of lossy and lossless image compression. [ABSTRACT FROM AUTHOR]

The on-board object detection of remote sensing images is of great significance and challenges in both military and civilian fields. To achieved on-board object detection, we require a high-performance and low power hardware architecture. Inspired by the hardware implementation advantages of the lifting scheme in JPEG2000, we introduce the lifting scheme into object detection networks to promote the hardware implementation capability. Based on the equivalence between the lifting scheme and the convolution, we proposed a lifting based FPGA accelerator. The lifting scheme provides a parallel strategy for every convolutional layer to obtain high performance, and high resource utilization. The proposed accelerator can improve the throughput of networks, and promote the utilization efficiency of hardware resources. The experimental results on lifting based YOLOv3-tiny (LS-YOLOv3-tiny) show that the proposed accelerator has better power efficiency than other hardware implementation, and have better DSP efficiency than other FPGA-based designs.

A new and an enriched JPEG algorithm is provided for identifying redundancies in a sequence of irregular noisy data points which also accommodates a reference-free criterion function. Our main contribution is by formulating analytically (instead of approximating) the inverse of the transpose of JPEG-wavelet transform without involving matrices which are computationally cumbersome. The algorithm is suitable for the widely-spread situations where the original data distribution is unobservable such as in cases where there is deficient representation of the entire population in the training data (in machine learning) and thus the covariate shift assumption is violated. The proposed estimator corrects for both biases, the one generated by endogenous truncation and the one generated by endogenous covariates. Results from utilizing 2 000 000 different distribution functions verify the applicability and high accuracy of our procedure to cases in which the disturbances are neither jointly nor marginally normally distributed.

Wavelet transform is a powerful tool for multiresolution time-frequency analysis. It has been widely adopted in many image processing tasks, such as denoising, enhancement, fusion, and especially compression. Wavelets lead to the successful image coding standard JPEG-2000. Traditionally, wavelets were designed from the signal processing theory with certain assumption on the signal, but natural images are not as ideal as assumed by the theory. How to design content-adaptive wavelets for natural images remains a difficulty. Inspired by the recent progress of convolutional neural network (CNN), we propose iWave as a framework for deriving wavelet-like transform that is more suitable for natural image compression. iWave adopts an update-first lifting scheme, where the prediction filter is a trained CNN, to achieve wavelet-like transform. The CNN can be embedded into a deep network that is analogous to an auto-encoder, which is trained end-to-end. The trained wavelet-like transform still possesses the lifting structure, which ensures perfect reconstruction, supports multiresolution analysis, and is more interpretable than the deep networks trained as “black boxes.” We perform experiments to verify the generality as well as the speciality of iWave in comparison with JPEG-2000. When trained with a generic set of natural images and tested on the Kodak dataset, iWave achieves on average 4.4% and up to 14% BD-rate reductions. When trained and tested with a specific kind of textures, iWave provides as high as 27% BD-rate reduction. [ABSTRACT FROM AUTHOR]

The measurement of carbon dioxide (CO2) concentration in exhaled air can be used as a diagnostic tool for many pulmonary and respiratory diseases. Capnography, the waveform that tracks the amount of CO2 in exhaled air, reflects the physiological and anatomical conditions of the human body. In this paper, we propose a novel speed of sound-based capnographic sensing mechanism for measuring the CO2 concentration in the exhaled air. The sensor response is further classified using our proposed second-generation convolutional neural network with SVM architecture. Automated quantitative analysis to differentiate between healthy subjects and patients with chronic obstructive pulmonary disease (COPD), between healthy and congestive heart failure (CHF), and between COPD and CHF has been implemented. The classification was performed on both the signal acquired using the developed hardware and the signal from the capnobase dataset. Furthermore, the experimental results were clinically validated. The proposed method yields an accuracy of 95.16%, 87.55%, and 90.19% for COPD/Healthy, CHF/Healthy, and COPD/CHF classifications, respectively. The development of a non-invasive, low cost, and small size sensor for capnography can extend the use of capnography from intubated patients to pre-hospital applications. Lower computation time and better accuracy achieved depict the effectiveness of the proposed method to be used in real-time diagnosis of cardiorespiratory diseases. [ABSTRACT FROM AUTHOR]

This brief proposes a modified version of the popular lifting algorithm of Daubechies-4 (D4) and Daubechies-6 (D6) wavelets and its efficient implementation using integer polynomial mapping (IPM). At first, an improved polyphase matrix for D4 is presented that eliminates one filter coefficient completely without losing any accuracy. Then, IPM is applied to encode the remaining irrational coefficients. As a result, computation error due to irrational numbers in the conventional method is significantly reduced, resulting in better image reconstruction. For D6, a two-level optimization scheme combined with the resource sharing of coefficients is applied that results in simplified hardware architecture with much fewer resources. [ABSTRACT FROM PUBLISHER]

Wavelet transform has been widely used in many signal and image processing applications. Due to its wide adoption for time-critical applications, such as streaming and real-time signal processing, many acceleration techniques were developed during the past decade. Recently, the graphics processing unit (GPU) has gained much attention for accelerating computationally-intensive problems and many solutions of GPU-based discrete wavelet transform (DWT) have been introduced, but most of them did not fully leverage the potential of the GPU. In this paper, we present various state-of-the-art GPU optimization strategies in DWT implementation, such as leveraging shared memory, registers, warp shuffling instructions, and thread- and instruction-level parallelism (TLP, ILP), and finally elaborate our hybrid approach to further boost up its performance. In addition, we introduce a novel mixed-band memory layout for Haar DWT, where multi-level transform can be carried out in a single fused kernel launch. As a result, unlike recent GPU DWT methods that focus mainly on maximizing ILP, we show that the optimal GPU DWT performance can be achieved by hybrid parallelism combining both TLP and ILP together in a mixed-band approach. We demonstrate the performance of our proposed method by comparison with other CPU and GPU DWT methods. [ABSTRACT FROM AUTHOR]

When working with the line-based wavelet transform, JPEG2000 needs to buffer many sub-band lines for future block coding. This coding buffer occupies the majority of the memory usage in a JPEG2000 system. In this paper, we first use the multilevel block-based wavelet transform, and then utilize the multilevel stripe-based wavelet transform to realize JPEG2000 coding. The system schemes of the multilevel block-based wavelet transform and the multilevel stripe-based wavelet transform for JPEG2000 are presented. The proposed schemes can effectively control the wavelet coefficient output pattern, and thus reduce the memory usage of JPEG2000 coding buffer. Compared with the line-based wavelet transform, the proposed schemes reduce more than 50% memory usage of the JPEG2000 system and slightly decrease the memory bandwidth. By holding the blocks/stripes in on-chip memory, which do not depend on the image size, the memory bandwidth of the JPEG2000 system can be further significantly reduced. The advantage of the stripe-based wavelet transform over the block-based wavelet transform lies in that it occupies much less internal memory. [ABSTRACT FROM AUTHOR]

In this brief, we propose a new parallel lifting-based 2-D DWT architecture with high memory efficiency and short critical path. The memory efficiency is achieved with a novel scanning method that enables tradeoff of external memory bandwidth and on-chip memory. Based on the data flow graph of the flipped lifting algorithm, processing units (PUs) are developed for maximally utilizing the inherent parallelism. With S number of PUs, the throughput can be scaled while keeping the latency constant. Compared with the best existing architecture, the proposed architecture requires less memory. For an N \times N image, the proposed architecture consumes a total of only 3N + 24S words of transposition memory, temporal memory, and pipeline registers. The synthesized results in a 90-nm CMOS process show that it achieves better area-delay products than the best existing design by 32.3%, 31.5%, and 27.0% when S = 2, 4, and 8, respectively, and by 26%, 26%, and 22% when the overhead for buffering the required overlapped pixels is taken into account. [ABSTRACT FROM AUTHOR]

This paper integrates Burt-Adelson's Laplacian pyramids with lifting schemes for the construction of slightly redundant decompositions. These decompositions implement multiscale smoothing on possibly non-equidistant point sets. Thanks to the slight redundancy and to the smoothing operations in the lifting scheme, the proposed construction unifies sparsity of the analysis, smoothness of the reconstruction and stability of the transforms. The decomposition is of linear computational complexity, with just a slightly larger constant than the fast lifted wavelet transform. This paper also discusses several alternatives in the design of non-stationary finite impulse response filters for a stable multiresolution smoothing system. These filters are adapted to each other and to the locations of the observations. [ABSTRACT FROM PUBLISHER]

In this paper, we present the lifting factorization and construction of wavelet bi-frames with arbitrary generators and scaling. We show that an arbitrary polyphase matrix of a wavelet bi-frame can be factorized into a series of lifting steps. Based on the proposed factorization, we present a general construction of bi-frames. Especially, we give an explicit formula to construct the bi-frames of two scaling and two generators with symmetry and one vanishing moment. This paper does not involve many theories of wavelet frames in mathematics, but focuses on the algebraic issues related to Laurent polynomials, which are efficient expressions in redundant filter banks associated with wavelet frames. As an extension of the classical two-channel filter bank, the redundant filter bank is more complicated but also more flexible. Furthermore, we present an algorithm to increase the number of vanishing moments to arbitrary order by lifting, which is iterated and is straightforward in implementation. [ABSTRACT FROM PUBLISHER]

It was proposed recently that quantized representations of the input source (e.g., images, video) can be used for the computation of the two-dimensional discrete wavelet transform (2D DWT) incrementally. The coarsely quantized input source is used for the initial computation of the forward or inverse DWT, and the result is successively refined with each new refinement of the source description via an embedded quantizer. This computation is based on the direct two-dimensional factorization of the DWT using the generalized spatial combinative lifting algorithm. In this correspondence, we investigate the use of prediction for the computation of the results, i.e., exploiting the correlation of neighboring input samples (or transform coefficients) in order to reduce the dynamic range of the required computations, and thereby reduce the circuit activity required for the arithmetic operations of the forward or inverse transform. We focus on binomial factorizations of DWTs that include (amongst others) the popular 9/7 filter pair. Based on an FPGA arithmetic co-processor testbed, we present energy-consumption results for the arithmetic operations of incremental refinement and prediction-based incremental refinement in comparison to the conventional (nonrefinable) computation. Our tests with combinations of intra and error frames of video sequences show that the former can be 70% more energy efficient than the latter for computing to half precision and remains 15% more efficient for full-precision computation. [ABSTRACT FROM AUTHOR]

We propose structural multidimensional multichannel filter banks with desirable numbers of vanishing moments for the analysis and synthesis banks. For a two-channel filter bank, we use a three-step lifting scheme as opposed to the conventional two-step lifting method in order to provide more symmetry between the analysis and synthesis filters. We show that the resulting filters have more regularity, lower frame bounds ratio, and better frequency selectivity. We also extend our design to a general multichannel framework. Three steps of lifting scheme provides us a degree of freedom which we can benefit from toward a more flexible design. [ABSTRACT FROM AUTHOR]

In this paper, we present the lifting scheme of wavelet bi-frames along with theory analysis, structure, and algorithm. We show how any wavelet bi-frame can be decomposed into a finite sequence of simple filtering steps. This decomposition corresponds to a factorization of a polyphase matrix of a wavelet bi-frame. Based on this concept, we present a new idea for constructing wavelet bi-frames. For the construction of symmetric bi-frames, we use generalized Bernstein basis functions, which enable us to design symmetric prediction and update filters. The construction allows more efficient implementation and provides tools for custom design of wavelet bi-frames. By combining the different designed filters for the prediction and update steps, we can devise practically unlimited forms of wavelet bi-frames. Moreover, we present an algorithm of increasing the number of vanishing moments of bi-framelets to arbitrary order via the presented lifting scheme, which adopts an iterative algorithm and ensures the shortest lifting scheme. Several construction examples are given to illustrate the results. [ABSTRACT FROM AUTHOR]

This correspondence discusses the reversible integer 16-point discrete Fourier transform (RiDFT) which uses integer operations with control bits. The decomposition of the RiDFT is based on the paired representation, when the Fourier transform is split recursively into a set of short transforms of orders 8, 4, 2, and 1. Control bits allow for inverting the integer approximations of multiplications by twiddle factors. The proposed 16-point RiDFT uses 16 operations of real multiplication and 62 additions. The integer approximation of the transform with eight control bits with additional two lifting schemes, which requires two more multiplications, is also considered. [ABSTRACT FROM AUTHOR]

We present in this paper a novel way to adapt multidimensional wavelet filter bank, based on the nonseparable lifting scheme framework, to any specific problem. It allows the design of filter banks with a desired number of degrees of freedom, while controlling the number of vanishing moments of the primal wavelet (Ñ moments) and of the dual wavelet (N moments). The prediction and update filters, in the lifting scheme based filter banks, are defined as Neville filters of order Ñ and N, respectively. However, in order to introduce some degrees of freedom in the design, these filters are not defined as the simplest Neville filters. The proposed method is convenient: the same algorithm is used whatever the dimensionality of the signal, and whatever the lattice used. The method is applied to content-based image retrieval (CBIR): an image signature is derived from this new adaptive nonseparable wavelet transform. The method is evaluated on four image databases and compared to a similar CBIR system, based on an adaptive separable wavelet transform. The mean precision at five of the nonseparable wavelet based system is notably higher on three out of the four databases, and comparable on the other one. The proposed method also compares favorably with the dual-tree complex wavelet transform, an overcomplete nonseparable wavelet transform. [ABSTRACT FROM AUTHOR]

This paper focuses on the construction of multidimensional biorthogonal multiwavelets and the perfect reconstruction multifilter banks. Based on the Hermite-Neville filter, two lifting structures have been proposed and systematically investigated, and a general design framework has been developed for building biorthogonal multiwavelets and Hermite interpolation filter banks with any multiplicity for any lattice in any dimension with any number of primal and dual vanishing moments. The construction is an important generalization of the Neville-based lifting scheme and inherits all of the advantages of lifting schemes such as fast transform, in-place computation and integer-to-integer transforms. Our multiwavelet systems preserve most of the desirable properties for applications, such as interpolating, short support, symmetry, and high vanishing moments. [ABSTRACT FROM AUTHOR]

In this brief an efficient folded architecture (EFA) for lifting-based discrete wavelet transform (DWT) is presented. The proposed EFA is based on a novel form of the lifting scheme that is given in this brief. Due to this form, the conventional serial operations of the lifting data flow can be optimized into parallel ones by employing parallel and pipeline techniques. The corresponding optimized architecture (OA) has short critical path latency and is repeatable. Further, utilizing this repeatability, the EFA is derived from the OA by employing the fold technique. For the proposed EFA, hardware utilization achieves 100%, and the number of required registers is reduced. Additionally, the shift-add operation is adopted to optimize the multiplication; thus, the proposed architecture is more suitable for hardware implementation. Performance comparisons and field-programmable gate array (FPGA) implementation results indicate that the proposed EFA possesses better performances in critical path latency, hardware cost, and control complexity. [ABSTRACT FROM AUTHOR]

The dual-tree complex wavelet transform (CWT) has recently received significant interest in the wavelet community, owing primarily to its directional selective and near-shift invariant properties. It has been shown that with two separate maximally decimated and dyadic decompositions where filters are offset by a half sample, the resulting CWT wavelet bases form an approximate Hilbert transform pair. In this paper, we present the design, implementation and applications of several families of orthogonal as well as biorthogonal rational-coefficient wavelet filters that satisfy the Hubert transform pair condition and meet other desirable properties such as high coding gain, good directional sensitivity, and sufficient degree of regularity. The wavelet filters presented here, which confirm to Selesnick's and Kingsbury's design schemes, are designed and implemented directly in the lattice and lifting domain using VLSI-friendly dyadic coefficients. We confirm the fact that rational-coefficient constraint does not impose a significant loss in terms of energy compaction, wavelet smoothness, time-invariance, or directionality. We also propose the time-reversal relationships between the two CWT filter pairs in lattice and lifting domain, thereby facilitating both the design and implementation process. In the end, we present several applications and evaluations to illustrate the performance of the proposed designs. [ABSTRACT FROM AUTHOR]

A useful tool to construct wavelet decompositions is the lifting scheme. The generalized lifting is an extension of the classical lifting scheme to introduce more flexibility and to permit the creation of new nonlinear and adaptive transforms. However, the design of generalized prediction and update steps is more involved. This letter proposes a generalized prediction design that minimizes the detail signal energy and entropy at the same time. Two algorithm variants are given. The fixed prediction uses the image class statistics to derive the optimal transform. If the statistics are unknown, the adaptive prediction extracts them from the image being coded. The resulting decompositions are applied to lossless image coding, reporting good results. The adaptive algorithm has no bookkeeping or side information requirements, yet its performance is close to the fixed prediction performance. [ABSTRACT FROM AUTHOR]

We present a signal processing approach using discrete wavelet transform (DWT) for the generation of complex synthetic aperture radar (SAR) images at an arbitrary number of dyadic scales of resolution. The method is computationally efficient and is free from significant system-imposed limitations present in traditional subaperture-based multiresolution image formation. Problems due to aliasing associated with biorthogonal decomposition of the complex signals are addressed. The lifting scheme of DWT is adapted to handle complex signal approximations and employed to further enhance the computational efficiency. Multiresolution SAR images formed by the proposed method are presented. [ABSTRACT FROM PUBLISHER]

This brief presents a novel very large-scale integration (VLSI) architecture for discrete wavelet packet transform (DWPT). By exploiting the in-place nature of the DWPT algorithm, this architecture has an efficient pipeline structure to implement high-throughput processing without any on-chip memory/first-in first out access. A folded architecture for lifting-based wavelet filters is proposed to compute the wavelet butterflies in different groups simultaneously at each decomposition level. According to the comparison results, the proposed VLSI architecture is more efficient than the previous proposed architectures in terms of memory access, hardware regularity and simplicity, and throughput. The folded architecture not only achieves a significant reduction in hardware cost but also maintains both the hardware utilization and high-throughput processing with comparison to the direct mapped tree-structured architecture. [ABSTRACT FROM AUTHOR]

Novel architectures for 1-D and 2-D discrete wavelet transform (DWT) by using lifting schemes are presented in this paper. An embedded decimation technique is exploited to optimize the architecture for 1-D DWT, which is designed to receive an input and generate an output with the low- and high-frequency components of original data being available alternately. Based on this 1-D DWT architecture, an efficient line-based architecture for 2-D DWT is further proposed by employing parallel and pipeline techniques, which is mainly composed of two horizontal filter modules and one vertical filter module, working in parallel and pipeline fashion with .100% hardware utilization. This 2-D architecture is called fast architecture (FA) that can perform J levels of decomposition for N * N image in approximately 2N²(1 - 4-J)/3 internal clock cycles. Moreover, another efficient generic line-based 2-D architecture is proposed by exploiting the parallelism among four subband transforms in lifting-based 2-D DWT, which can perform J levels of decomposition for N * N image in approximately N2 (1 -4-J)/3 internal clock cycles; hence, it is called high-speed architecture. The throughput rate of the latter is increased by two times when comparing with the former 2-D architecture, but only less additional hardware cost is added. Compared with the works reported in previous literature, the proposed architectures for 2-D DWT are efficient alternatives in tradeoff among hardware cost, throughput rate, output latency and control complexity, etc. [ABSTRACT FROM AUTHOR]

In this paper, lossless audio coding using the integer modified discrete cosine transform (IntMDCT) is discussed. The IntMDCT is constructed as an integer approximation of the MDCT using the lifting scheme and is reversible. The rounding error shape of the IntMDCT is derived. When the spectral energy of the input audio signal is concentrated at the low frequencies, the rounding error spectrum limits the lossless coding performance. A method for shaping the rounding error in the transform domain is presented. This rounding error shaping scheme manipulates the error so that it is below the spectral envelope of the signal at the high frequencies in order to improve the lossless coding performance for the signal. Examples of an error shaping filter design are presented and verified by simulations. An IntMDCT-based lossless coding implementation is carried out to illustrate the use of the error shaping filters. [ABSTRACT FROM AUTHOR]

The lifting scheme is well known to be an efficient tool for constructing second generation wavelets and is often used to design a class of biorthogonal wavelet filter banks. For its efficiency, the lifting implementation has been adopted in the inter- national standard JPEG2000. It is known that the orthogonality of wavelets is an important property for many applications. This paper presents how to implement a class of infinite-impulse-response (IIR) orthogonal wavelet filter banks by using the lifting scheme with two lifting steps. It is shown that a class of IIR orthogonal wavelet filter banks can be realized by using allpass filters in the lifting steps. Then, the design of the proposed IIR orthogonal wavelet filter banks is discussed. The designed IIR orthogonal wavelet filter banks have approximately linear phase responses. Finally, the proposed IIR orthogonal wavelet filter banks are applied to the image compression, and then the coding performance of the proposed IIR filter banks is evaluated and compared with the conventional wavelet transforms. [ABSTRACT FROM AUTHOR]

The objective of this paper is to propose an efficient model-based bit allocation process optimizing the performances of a wavelet coder for semiregular meshes. More precisely, this process should compute the best quantizers for the wavelet coefficient subbands that minimize the reconstructed mean square error for one specific target bitrate. In order to design a fast and low complex allocation process, we propose an approximation of the reconstructed mean square error relative to the coding of semiregular mesh geometry. This error is expressed directly from the quantization errors of each coefficient subband. For that purpose, we have to take into account the influence of the wavelet filters on the quantized coefficients. Furthermore, we propose a specific approximation for wavelet transforms based on lifting schemes. Experimentally, we show that, in comparison with a "naïve" approximation (depending on the subband levels), using the proposed approximation as distortion criterion during the model-based allocation process improves the performances of a wavelet-based coder for any model, any bitrate, and any lifting scheme. [ABSTRACT FROM AUTHOR]

A flipping structure for the discrete wavelet transform (DWT) was proposed by Huang et al., which aimed at shortening the critical path of the lifting-based one-dimensional (1-D) architecture and reducing the number of pipeline register used in 1-D architecture as well as the size of temporal buffer (TB) required in the line-based two-dimensional (2-D) architecture. In this paper, we explore the intrinsic relationship between the size of TB required in the line-based architecture for 2-D DWT (LBA2DDWT) and the number of registers used in a 1-D DWT module, and present a modified view on this issue that has been proposed by Huang et al. An improved method of mapping the registers used in 1-D DWT architecture to the TB required in LBA2DDWT is presented, which reduces more efficiently the size of memory required in LBA2DDWT than Huang's method. A parallel-based lifting scheme (PLS) for DWT and its VLSI architecture are presented. The proposed PLS of DWT not only reduces efficiently the critical path but also results in the equality of the forward discrete wavelet transform and in- verse discrete wavelet transform implementations. Compared with Huang's method, the proposed PLS is more efficient in reducing critical path and/or the number of registers of VLSI implementation for 1-D DWT, and it can be concluded that the flipping structure of DWT is a special case of the PLS-based implementation. [ABSTRACT FROM AUTHOR]

Efficient line-based architectures for two-dimensional discrete wavelet transform (2-D DWT) are presented in this paper. We first present a four-input/four-output architecture for direct 2-D DWT that 1-level decomposition of a N × N image could be performed in approximately N2 /4 intra-working clock cycles (ccs), where the parallelism among four subbands transforms in lifting-based 2-D DWT is explored. By using this four-input/four- output architecture, we propose a novel pipelined architecture for multilevel 2-D DWT that can perform a complete dyadic decomposition of N × N image in approximately N2 /4 ccs. Performance analysis and comparison results demonstrate that, the proposed architectures have faster throughput rate and good performance in terms of production of throughput rate and hardware cost, as well as hardware utilization. The proposed pipelined architecture could be an efficient alternative for high-speed and/or low-power applications. [ABSTRACT FROM AUTHOR]

The proliferation of digital information in our society has enticed a lot of research into data-embedding techniques that add information to digital content, like images, audio, and video. In this paper, we investigate high-capacity lossless data-embedding methods that allow one to embed large amounts of data into digital images (or video) in such a way that the original image can be reconstructed from the watermarked image. We present two new techniques: one based on least significant bit prediction and Sweldens' lifting scheme and another that is an improvement of Tian's technique of difference expansion. The new techniques are then compared with various existing embedding methods by looking at capacity-distortion behavior and capacity control. [ABSTRACT FROM AUTHOR]

In this paper, a novel lifting integer wavelet transform based on difference correlation structure (DCCS-LIWT) is proposed. First, we establish a relationship between the performance of a linear predictor and the difference correlations of an image. The obtained results provide a theoretical foundation for the following construction of the optimal lifting filters. Then, the optimal prediction lifting coefficients in the sense of least-square prediction error are derived. DCCS-LIWT puts heavy emphasis on image inherent dependence. A distinct feature of this method is the use of the variance-normalized autocorrelation function of the difference image to construct a linear predictor and adapt the predictor to varying image sources. The proposed scheme also allows respective calculations of the lifting filters for the horizontal and vertical orientations. Experimental evaluation shows that the proposed method produces better results than the other well-known integer transforms for the lossless image compression. [ABSTRACT FROM AUTHOR]

In this paper, three generic RAM-based architectures are proposed to efficiently construct the corresponding two-dimensional architectures by use of the line-based method for any given hardware architecture of one-dimensional (1-D) wavelet filters, including conventional convolution-based and lifting-based architectures. An exhaustive analysis of two-dimensional architectures for discrete wavelet transform in the system view is also given. The first proposed architecture is for 1-level decomposition, which is presented by introducing the categories of internal line buffers, the strategy of optimizing the line buffer size, and the method of integrating any 1-D wavelet filter. The other two proposed architectures are for multi-level decomposition. One applies the recursive pyramid algorithm directly to the proposed 1-level architecture, and the other one combines the two previously proposed architectures to increase the hardware utilization. According to the comparison results, the proposed architecture outperforms previous architectures in the aspects of lint buffer size, hardware cost, hardware utilization, and flexibility. [ABSTRACT FROM AUTHOR]

A low-power, high-speed architecture which performs two-dimension forward and inverse discrete wavelet transform (DWT) for the set of filters in JPEG2000 is proposed by using a line-based and lifting scheme. It consists of one row processor and one column processor each of which contains four sub-filters. And the row processor which is time-multiplexed performs in parallel with the column processor Optimized shift-add operations are substituted for multiplications, and edge extension is implemented by embedded circuit The whole architecture which is optimized in the pipeline design way to speed up and achieve higher hardware utilization has been demons Stated in FPGA. Two pixels per clock cycle can be encoded at 100MHz. The architecture can be used as a compact and independent IP core for JPEG2000 VLSI implementation and various real-time image/video applications. [ABSTRACT FROM AUTHOR]

High-fidelity wavelet transforms can facilitate visualization and analysis of large scientific data sets. However, it is important that salient characteristics of the original features be preserved under the transformation. We present a set of filter design axioms in the spatial domain which ensure that certain feature characteristics are preserved from scale to scale and that the resulting filters correspond to wavelet transforms admitting in-place implementation. We demonstrate how the axioms can be used to design linear feature-preserving filters that are optimal in the sense that they are closest in L² to the ideal low pass filter. We are particularly interested in linear wavelet transforms for large data sets generated by computational fluid dynamics simulations. Our effort is different from classical filter design approaches which focus solely on performance in the frequency domain. Results are included that demonstrate the feature-preservation characteristics of our filters. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for the design of lifting filters to compute a multidimensional nonseparable wavelet transform. Our approach is stated in the general case, and is illustrated for the 2-D separable and for the quincunx images. Results are shown for the JPEG2000 database and for satellite images acquired on a quincunx sampling grid. The design of efficient quincunx filters is a difficult challenge which has already been addressed for specific cases. Our approach enables the design of less expensive filters adapted to the signal statistics to enhance the compression efficiency in a more general case. It is based on a two-step lifting scheme and joins the lifting theory with Wiener's optimization. The prediction step is designed in order to minimize the variance of the signal, and the update step is designed in order to minimize a reconstruction error. Application for lossy compression shows the performances of the method. [ABSTRACT FROM AUTHOR]

The lifting scheme reduces the computational complexity of the discrete wavelet transform (DWT) by factoring the wavelet filters into cascades of simple lifting steps that process the input samples in pairs. We propose four compact and efficient hardware architectures for implementing lifting-based DWTs, namely, one-dimensional (1-D) and two-dimensional (2-D) versions of what we call recursive and dual scan architectures. The 1-D recursive architecture exploits interdependencies among the wavelet coefficients by interleaving, on alternate clock cycles using the same datapath hardware, the calculation of higher order coefficients along with that of the first-stage coefficients. The resulting hardware utilization exceeds 90% in the typical case of a five-stage 1-D DWT operating on 1024 samples. The 1-D dual scan architecture achieves 100% datapath hardware utilization by processing two independent data streams together using shared functional blocks. The recursive and dual scan architectures can be readily extended to the 2-D case. The 2-D recursive architecture is roughly 25% faster than conventional implementations, and it requires a buffer that stores only a few rows of the data array instead of a fixed fraction (typically 25% or more) of the entire array. The 2-D dual scan architecture processes the column and row transforms simultaneously, and the memory buffer size is comparable to existing architectures. [ABSTRACT FROM AUTHOR]