nils-herrmann/clean_scirepeval huggingface dataset:
Shift invariant null filter for the suppression of bandlimited impulse signal of unknown occurrence-time

A detector structure and a new configuration for a parameter-invariant null filter (PINF) are proposed for the reception of signals in a background of bandlimited impulsive noise. The concept of PINF is extended to the case of wideband signals, such as broad-tailed impulsive noise formed by the response of an ideal LPF to a train of delta functions of unknown time occurrences. The proposed PINF provides efficient and robust performance for signal detection in the presence of such interference. The performance of the resulting algorithm is analyzed using computer simulation, and the results are compared to those of the conventional algorithm used for solving the same problem.<<ETX>>
A variable quantization technique for image compression using integer Tchebichef transform

In the field of image and data compression there is always a need for novel transform coding techniques promising improved reconstruction and reduced computational complexity. The usage of integer adaptation of the popular discrete cosine transform (DCT) with fixed quantization is prevalent in the field of video compression due to its ease of computation and acceptable performance. However, there exist other polynomial-based orthogonal transforms like discrete Tchebichef transform (DTT), which possess valuable properties like energy compaction, but are potentially unexploited in comparison. The influence of specific features, such as the structure and content, of the image on the quality of reconstructed image after decompression is undeniable. This paper aims to harness this aspect and introduces a technique to adapt the quantization performed during compression according to the characteristics of the image block without any additional computational or transmission overhead. The image compression performance of integer DTT and integer DCT, using both variable and fixed quantization, are evaluated and compared.
A fast 8×8 transform for image compression

In this paper, we propose an efficient 8×8 transform matrix for image compression by appropriately introducing some zeros in the 8×8 signed DCT matrix. We show that the proposed transform is orthogonal, which is a highly desirable property. In order to make this novel transform more attractive for recent real-time applications, we develop an efficient algorithm for its fast computation. By using this algorithm, the proposed transform requires only 18 additions to transform an 8-point sequence. Compared to the existing 8×8 approximated DCT matrices, it is shown that savings of 25% in the number of arithmetic operations can easily be achieved using the proposed transform operator without noticeable degradations in the reconstructed images. We also present simulation results using some standard test images to show the efficiency of the proposed transform in image compression.
Spatially adaptive thresholding in wavelet domain for despeckling of ultrasound images

Ultrasound imaging is widely used for diagnostic purposes among the clinicians. A major problem concerning the ultrasound images is their inherent corruption by the multiplicative speckle noise that hampers the quality of the diagnosis, and reduces the efficiency of the algorithms for automatic image processing. In this paper, we propose a new spatially adaptive wavelet-based method in order to reduce the speckle noise from ultrasound images. A spatially adaptive threshold is introduced for denoising the coefficients of log-transformed ultrasound images. The threshold is obtained from a Bayesian maximum a posteriori estimator that is developed using a symmetric normal inverse Gaussian probability density function (PDF) as a prior for modelling the coefficients of the log-transformed reflectivity. A simple and fast method is provided to estimate the parameters of the prior PDF from the neighbouring coefficients. Extensive simulations are carried out using synthetically speckled and ultrasound images. It is shown that the proposed method outperforms several existing techniques in terms of the signal-to-noise ratio, edge preservation index and structural similarity index and visual quality, and in addition, is able to maintain the diagnostically significant details of ultrasound images.
An efficient pyramid for motion estimation

It is shown in this paper that by choosing an appropriate value for the parameter a of the generating kernel in constructing Burt and Adelson's pyramid for motion estimation, one can eliminate the operation of floating point multiplication needed in such a construction, and reduce the computational load to the same order as in the case of the mean pyramid. It is further shown through simulation studies that this new kernel does not degrade the performance of motion estimation, as compared to that using kernels giving the best performance.
Motion Adaptive Video Denoising in the Wavelet Domain Based on Bivariate Shrinkage

This paper proposes a new wavelet-based video noise reduction algorithm. Each frame in the video sequence is transformed to the wavelet domain using the dual-tree complex wavelet transform. We model the interscale dependencies in subband representation of each frame using a non-Gaussian bivariate distribution. The parameters for this bivariate distribution are estimated adaptively using the local correlations that exist between neighboring coefficients within each subband. Based on this bivariate distribution a shrinkage function is developed using the maximum a posteriori (MAP) rule. To improve the performance of the filter, information from the adjacent frames is also incorporated in the shrinkage function. This is achieved by detecting the motion between the corresponding subband coefficients in successive frames. Experimental results show that the proposed scheme outperforms several state-of-the-art spatio-temporal filters in terms of the peak signal to noise ratio and the visual quality.
Semi-Hurwitz polynomials and their properties

Hurwitz polynomials find applications in the stability studies of dynamic systems. In this paper two-variable (2-V) semi-Hurwitz polynomials are defined and their application in the study of the stability of two-dimensional (2-D) half-plane filters is illustrated. Four types of semiHurwitz polynomials are identified and a number of properties of very strict semi-Hurwitz polynomials are presented.
Efficient pruning algorithms for the DFT computation for a subset of output samples

This paper presents efficient pruning algorithms for computing the DFT for a subset of output samples based on radix-2 decimation-in-time and decimation-in-frequency FFTs. They provide efficient implementations with a minimum number of stages. Comparisons are made with previously reported algorithms in terms of the computational complexity. The proposed algorithms are shown to provide a substantial reduction in the number of arithmetic operations, data transfers, address computations, and twiddle factor evaluations or accesses to the lookup table. The proposed algorithms retain all the features and characteristics, such as the simplicity and regularity, of the well-known Cooley-Tukey radix-2 FFT algorithms.
Optimum multiplicative watermark detector in contourlet domain using the normal inverse Gaussian distribution

Digital watermarking has been widely used in the copyright protected images in multimedia. This paper addresses the blind watermark detection problem in contourlet domain. It is known that the contourlet coefficients of images have non-Gaussian property and can be well modelled by non-Gaussian distributions such as the normal inverse Gaussian (NIG). In view of this, we exploit this model to derive closed-form expressions for the test statistics and design an optimum blind watermark detector in the contourlet domain. Through conducting several experiments, the performance of the proposed detector is evaluated in terms of the probabilities of detection and false alarm and compared to that of the other existing detectors. It is shown that the proposed detector using the NIG distribution is superior to other detectors in terms of providing higher rate of detection. It is also shown that the proposed NIG-based detector is more robust than other detectors against attacks, such as JPEG compression and Gaussian noise.
Overlapped variable size block motion compensation

The conventional fixed-size block motion compensation technique has two major drawbacks. One is the blocking effect that is visually annoying along with a residual image that is difficult to compress. The other is an ineffective and inefficient representation of the motion information because of the non-adaptive frame partitioning. This paper presents a new method that can adaptively divide a frame according to its motion contents and also effectively reduce the blocking effect by applying a windowing technique. Simulation results show that the proposed method can significantly improve the visual quality of the prediction image and increase the coding efficiency.
On the modeling of the wavelet image coefficients using a symmetric generalized hyperbolic PDF

Appropriate modelling of the wavelet image coefficients is very important for developing efficient methods for wavelet-based image processing tasks such as compression and denoising. In this paper, we introduce the symmetric generalized hyperbolic probability density function (PDF) as a suitable prior for modelling the distribution of the wavelet image coefficients. A simple moment-based method is presented to estimate the parameters of the proposed PDF, and the effectiveness of the proposed method for parameter estimation studied using Monte-Carlo simulations. The appropriateness of the SGH density as a prior PDF is also demonstrated.
Analytical methods for the design of 2-D elliptically symmetric digital filters using McClellan transformation

Some analytical methods are developed for choosing the coefficients of the first-order McClellan transformation for the design of two-dimensional elliptically symmetric digital filters from one-dimensional digital filters. Several extremely simple formulas are presented for both the original and the scaling-free coefficients for all the cases of both the vertical and the horizontal elliptical cutoff contours. The formulas are derived by forcing the lower-order significant terms in the power series expansion of the linear error function to zero. Many examples are given. The results of these methods agree very well with the results of the optimization methods, and the accuracy of the formulas is more than sufficient for many engineering applications. The formulas are very useful for real-time adaptive filter design and filtering by low-cost, stand-alone two-dimensional signal processors. The number of multiplications required to implement the designed filters is shown to be small.<<ETX>>
Concealment of interpolation errors for low bit-rate motion-compensated interpolation

In this paper, we propose a low cost motion-compensated interpolation technique to improve the video quality for the low bit-rate video encoded in conjunction with frame dropping. The proposed approach exploits the block-based motion vector field available to the decoder to avoid the complex motion estimation at the receiver. An iterative refinement technique derived using the finite element method is employed to efficiently conceal the interpolation errors caused by unfilled and overlapped pixels in the predicted frames. Consequently, no pixel classification is needed in the proposed technique, thus substantially reducing the computational complexity. Simulation results show that this technique results in reconstructed frames with good visual quality.
A new improved model-based seizure detection using statistically optimal null filter

A patient-specific model-based seizure detection method using statistically optimal null filters (SONF) has been recently proposed to aid the review of long-term EEG [1, 2]. The method relies on the model of a priori known seizure (template pattern) for subsequent detection of similar seizures. Artifacts, non-epileptic EEG rhythms, and at times modeling errors lead to increased false or missed detections. In this paper, we present a new improved model-based seizure detection that introduces a pre-processing block for artifact rejection, an adaptive technique of modeling the template patterns, and a new evolution-based classifier. The proposed classifier tracks the temporal evolution of seizure to improve the classification accuracy. With the help of simulated EEG, we illustrate the significance and need for these modifications. Further, performance of the complete algorithm is tested on single channel depth EEG of seven patients, and compared with the previous approaches. In terms of sensitivity and specificity, the proposed method resulted in 84% and 100%, method of  65% and 84%, and method of , 84% and 90% respectively. An overall performance improvement is seen as enhanced detection sensitivity and reduced false positives. This is preliminary result on seven patient data.
An area-reduced scheme for modulo 2/sup n/-1 addition/subtraction

In this paper, we present a versatile area-reduced scheme for modulo 2/sup n/-1 adders and subtracters using a MUX-based increment/decrement algorithm. A FPGA-based comparison of the proposed modulo adder and the conventional modulo adder designs is carried out. The implementation results show that the proposed adder reduces the area close to 30% compared with the modulo adder of Bayoumi et al. The delay and the power are also reduced around 10%. In addition, it is also shown that the proposed design requires less hardware resources than the parallel-prefix modulo adder of Kalampoukas et al. while providing a comparable operation speed.
Realization of 2D FIR filters using generalized polyphase structure combined with singular-value decomposition

In this paper, a realization scheme that combines the singular-value decomposition (SVD) and the generalized polyphase (GP) structure is proposed for 2D linear-phase FIR filters. With a small number of extra additions, a high-order 2D FIR filter is converted to several lower-order 2D subfilters. These subfilters are then realized using the SVD, yielding a parallel implementation structure for each 2D subfilter which consists of a number of 1D short-tap FIR filters. Due to the energy compaction of the SVD and the frequency-selective property of the GP structure, the number of parallel branches in each 2D subfilter is significantly reduced without introducing a large error. It is also shown that the various symmetries of 2D filters, such as the quadrantal symmetry and the central symmetry, are well preserved in the proposed GP-SVD structure
New windowing techniques for variable-size block motion compensation

The conventional overlapped block motion compensation (OBMC) technique, although effective in reducing the blocking effect for fixed-size partitioned frames, cannot be readily used for a frame partitioned region-wise or using variable-size blocks. The generalised OBMC (GOBMC) method, even though generally applicable, is not very effective in reducing the blocking effect and prediction error. Two windowing techniques are presented to reduce the blocking effect for a frame partitioned region-wise or using variable-size blocks. In the first technique, a virtual re-partitioning operation is employed, which maps a partitioned frame into its corresponding fully partitioned frame at the bottom level of the quadtree so that each resulting block has eight neighbouring blocks. In the second technique, the virtual re-partitioning operation for a given block (region) is carried out adaptively and performed locally; however, blocks (regions) need not always be virtually partitioned to the bottom level. Compared to the GOBMC method, the proposed techniques make use of more pixels in the close vicinity of boundaries of the regions in the windowing operation. Simulation results are included of applying the proposed techniques on a number of MPEG video sequences. These results indicate that the proposed techniques are superior to the GOBMC method in terms of reducing the prediction error as well as the blocking effect.
Systolic algorithms and a memory-based design approach for a unified architecture for the computation of DCT/DST/IDCT/IDST

In this paper, an efficient design approach for a unified very large-scale integration (VLSI) implementation of the discrete cosine transform/discrete sine transform/inverse discrete cosine transform/inverse discrete sine transform based on an appropriate formulation of the four transforms into cyclic convolution structures is presented. This formulation allows an efficient memory-based systolic array implementation of the unified architecture using dual-port ROMs and appropriate hardware sharing methods. The performance of the unified design is compared to that of some of the existing ones. It is found that the proposed design provides a superior performance in terms of the hardware complexity, speed, I/O costs, in addition to such features as regularity, modularity, pipelining capability, and local connectivity, which make the unified structure well suited for VLSI implementation.
Discrete tchebichef transform-A fast 4x4 algorithm and its application in image/video compression

Discrete Tchebichef transform (DTT), derived from a discrete class of the popular Chebyshev polynomials, is a novel orthogonal transform that has high energy compaction and de-correlation properties. Therefore, in this paper, DTT is examined and treated for transform coding applications. A framework is laid to derive an approximation- free integer representation of DTT to meet the current application requirements. A fast algorithm is further proposed for multiplier-free computation of DTT. The image compression performance of the 4- point DTT is found to be superior to that of the 4-point discrete cosine transform (DCT) and integer cosine transform (ICT), the integer approximation of DCT. It is shown that the fast DTT is easily derived, has low complexity, does not involve approximations and can be carried out within the same dynamic range. Hence, DTT can be used for image and data compression applications. Since the image compression performance and computational simplicity of DTT are found to be significantly better than that of ICT, the use of DTT in place of ICT for transform coding in the H.264/AVC looks promising.
Effects of errors and error recovery in images compressed by the JPEG still image compression standard algorithm

The use of the variable-length coding in the final stage of image compression using JPEG makes the image more sensitive to channel errors and can have severe effects on the viewed image. This is due to loss of synchronization in the decoder. Even one bit error can propagate significantly throughout the image. In the past, some techniques have been proposed for resynchronizing Huffman decoders using special synchronizing codewords. The JPEG standard itself allows the use of a special restart marker to help decoder resynchronization. It does not, however, give any guidelines for error recovery. We first describe the most probable types of errors that occur in a JPEG data stream. We focus on techniques and algorithms for detecting the occurrence of a particular error and then for locating that error. One technique functions at the entropy encoding level by taking advantage of the specific data structure of the JPEG stream and using alternately two different end-of-block characters. Others function at the DCT coefficient level or at the pixel level, detecting unlikely patterns that are produced due to errors. We compare different methods and finally propose the most effective method for error detection and image correction.<<ETX>>
Filtered gradient algorithms applied to a subband adaptive filter structure

Adaptive filtering techniques in subbands have been recently developed for a number of applications including acoustic echo cancellation and wideband active noise control. In such applications, hundreds of taps are required resulting in high computational complexity and low convergence rate when using LMS-based algorithms. For fullband systems, new algorithms which try to overcome these drawbacks have been investigated. A class of these algorithms employing variants of the filtered gradient adaptive (FGA) algorithm has been successfully developed. We apply these techniques to a recently proposed subband adaptive filter structure in order to improve the convergence rate and the computational load. Computer simulations show the benefits obtained with these proposed algorithms.
A New Class of Reciprocal–Orthogonal Parametric Transforms

In this paper, a new class of reciprocal-orthogonal parametric (ROP) transforms having 3N/2 independent parameters for a sequence length N that is a power of two is proposed. The basic idea behind the proposed transforms is to appropriately combine a new parametric kernel with that of the well-known Walsh-Hadamard transform that results in a square parametric matrix operator of order N with some very interesting properties. It is shown that the inverse matrix operator of the proposed class of transforms can be easily obtained by taking the reciprocal of each of the entries of the forward matrix and then transposing the resulting matrix. In addition, a simple method is introduced in order to facilitate the generation of the matrix operator of the proposed ROP transforms. This method is then used specifically to construct new classes of unitary and multiplication-free transforms. Many other new transforms, as well some of the existing ones, can be derived from these proposed ROP transforms. An efficient algorithm is developed for a fast computation of the proposed transforms. In view of the availability of this fast algorithm and the property of easily computable inverse transform, the proposed ROP transforms can be used in many transform-based applications, with their independent parameters providing more degrees of freedom such as affording an additional secret key in watermarking and encryption applications.
Block Tree Partitioning for Wavelet Based Color Image Compression

This paper presents a new algorithm for wavelet based image compression by exploiting zero tree concept in the wavelet decomposed image. The algorithm has a big edge over previously developed wavelet based image compression algorithms in that it utilizes inter and intra band correlation simultaneously, something that previous algorithms failed to exploit. Besides the improvement in coding efficiency, the algorithm also uses significantly lower memory for computation and coding thereby reducing the complexity of the algorithm. The striking feature of the algorithm is pass independent coding that makes it suitable for application to error protection schemes and makes it less vulnerable to data loss due to noisy communication channel. The algorithm codes all the color bands independently thus enabling differential coding for the color information. The paper starts with the discussion of the concept underlying the algorithm and then sees the algorithm in a broader light. Comparisons have been made to SPIHT, the well known zero tree coder for wavelet based image compression in terms of coding efficiency, memory requirements and error resiliency
Sensitivity invariants for linear time-invariant networks

For a general linear time-invariant network it is shown that the sum of the sensitivities, of any order, of a network function over different parameter sets is invariant. An expression is derived for calculating the sum of (k+l) th-order sensitivities knowing the same for k th-order sensitivities. It is also shown that lower bounds for the quadratic sensitivity index may be obtained by using the firstorder sensitivity invariants.