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A CR-self-adjusting data compression algorithm based on complex Fourier transform is presented in this paper. Because of the application of CFFT algorithm, the CR is doulbled with high fidelity without the operations of QRS complex detection, wave form analysis and difference calculation.
We use the traveling front approach to derive exact asymptotic results for the statistics of the number of particles in a class of directed diffusion-limited aggregation models on a Cayley tree. We point out that some aspects of these models are closely connected to two different problems in computer science, namely, the digital search tree problem in data structures and the Lempel-Ziv algorithm for data compression. The statistics of the number of particles studied here is related to the statistics of height in digital search trees which, in turn, is related to the statistics of the length of the longest word formed by the Lempel-Ziv algorithm. Implications of our results to these computer science problems are pointed out.
We propose two new image compression-decompression methods that reproduce images with better visual fidelity, less blocking artifacts, and better PSNR, particularly in low bit rates, than those processed by the JPEG Baseline method at the same bit rates. The additional computational cost is small, i.e., linearly proportional to the number of pixels in an input image. The first method, the "full mode" polyharmonic local cosine transform (PHLCT), modifies the encoder and decoder parts of the JPEG Baseline method. The goal of the full mode PHLCT is to reduce the code size in the encoding part and reduce the blocking artifacts in the decoder part. The second one, the "partial mode" PHLCT (or PPHLCT for short), modifies only the decoder part, and consequently, accepts the JPEG files, yet decompresses them with higher quality with less blocking artifacts. The key idea behind these algorithms is a decomposition of each image block into a polyharmonic component and a residual. The polyharmonic component in this paper is an approximate solution to Poisson's equation with the Neumann boundary condition, which means that it is a smooth predictor of the original image block only using the image gradient information across the block boundary. Thus, the residual--obtained by removing the polyharmonic component from the original image block--has approximately zero gradient across the block boundary, which gives rise to the fast-decaying DCT coefficients, which, in turn, lead to more efficient compression-decompression algorithms for the same bit rates. We show that the polyharmonic component of each block can be estimated solely by the first column and row of the DCT coefficient matrix of that block and those of its adjacent blocks and can predict an original image data better than some of the other AC prediction methods previously proposed. Our numerical experiments objectively and subjectively demonstrate the superiority of PHLCT over the JPEG Baseline method and the improvement of the JPEG-compressed images when decompressed by PPHLCT.
Recently, HOLTER product is developing to long time recording. In order to decrease the cost, compression of ECG data is needed. It is done first by some kind of DPCM to decrease the range of the data Value, then by using suitable arithmetic of HUFFMAN coding's and Arithmetic coding's. All the methods can be realized in microprocessor and get compression ratio of 2-4.
This paper presents novel coding algorithms based on tree-structured segmentation, which achieve the correct asymptotic rate-distortion (R-D) behavior for a simple class of signals, known as piecewise polynomials, by using an R-D based prune and join scheme. For the one-dimensional case, our scheme is based on binary-tree segmentation of the signal. This scheme approximates the signal segments using polynomial models and utilizes an R-D optimal bit allocation strategy among the different signal segments. The scheme further encodes similar neighbors jointly to achieve the correct exponentially decaying R-D behavior (D(R) - c(o)2(-c1R)), thus improving over classic wavelet schemes. We also prove that the computational complexity of the scheme is of O(N log N). We then show the extension of this scheme to the two-dimensional case using a quadtree. This quadtree-coding scheme also achieves an exponentially decaying R-D behavior, for the polygonal image model composed of a white polygon-shaped object against a uniform black background, with low computational cost of O(N log N). Again, the key is an R-D optimized prune and join strategy. Finally, we conclude with numerical results, which show that the proposed quadtree-coding scheme outperforms JPEG2000 by about 1 dB for real images, like cameraman, at low rates of around 0.15 bpp.
This article presents an improved morphological image representation that can be used for image compression, obtaining very high compression rates. The new image representation described in this work is called skeleton structure and is a natural extension of the morphologic structure. This article will present its theoretical background, introduce the new representation, and show some application examples.
We performed visual comparison of 200 head magnetic resonance (MR) and 200 head computed tomography (CT) images compressed at two levels using standard Joint Photographic Experts Group (JPEG) irreversible compression and a preliminary version of the JPEG 2000 irreversible algorithm. Blinded evaluations by neuroradiologists compared original versus either JPEG or JPEG 2000. We found that this version of JPEG 2000 did not perform as well as the current JPEG for head CTs, but for MR images, JPEG 2000 performed as well or better. Around 7:1 compression ratio seemed to be a conservative point where there was no perceptible difference.
The application of a lossless compressioin algorithm, based on the R, G, B value of pixel, to a X ray analyzing system is introduced through some medical examples of the X Ray image. The realization of the algorithm is completely presented, based on the design and the description of the characteristics, the R, G, B value of the pixed and the lossless compression methods of the X ray images.
In this work we tackle the problem of applying to echographic imaging those synthetic aperture focusing techniques (SAFT) in the frequency domain commonly used in the field of synthetic aperture radars (SAR). The aim of this research is to improve echographic image resolution by using chirp transmit signals, and by performing pulse compression in both dimensions (depth and lateral). The curved geometry present in the unfocused radio-frequency (RF) ultrasonic image is the main cause of inaccuracy in the direct application of frequency domain SAFT algorithms to echographic imaging. The focusing method proposed in this work, after pulse compression in the depth dimension, performs lateral focusing in the mixed depth-lateral spatial frequency domain by means of a depth variant remapping followed by lateral pulse compression. This technique has the advantage of providing a resolution that is uniform in nonfrequency selective attenuation media, and improved with respect to conventional time domain SAFT, without requiring the acquisition and processing of channel data necessary for the most advanced synthetic transmit aperture techniques. Therefore, the presented method is suitable for easy real-time implementation with current generation hardware.
Eight different properties of three classes of compounds, metal halides, halomethanes, and chlorofluorocarbons, have been modeled with the aim to check the validity of the odd complete graph conjecture suggested for encoding the contribution of the inner-core electrons to the molecular connectivity indices. Modeling using this conjecture is compared with modeling using connectivity indices derived by other well-known algorithms. The conjecture of odd complete graph for the inner-core electrons achieves to improve the modeling quality of the molecular connectivity indices and/or of the molecular connectivity terms. The importance of the recently introduced dual molecular connectivity indices and pseudoindices in further refining the modeling quality of the higher-order terms has also been stressed.
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Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
A wavelet-based electrocardiogram (ECG) data compression algorithm is proposed in this paper. The ECG signal is first preprocessed, the discrete wavelet transform (DWT) is then applied to the preprocessed signal. Preprocessing guarantees that the magnitudes of the wavelet coefficients be less than one, and reduces the reconstruction errors near both ends of the compressed signal. The DWT coefficients are divided into three groups, each group is thresholded using a threshold based on a desired energy packing efficiency. A binary significance map is then generated by scanning the wavelet decomposition coefficients and outputting a binary one if the scanned coefficient is significant, and a binary zero if it is insignificant. Compression is achieved by 1) using a variable length code based on run length encoding to compress the significance map and 2) using direct binary representation for representing the significant coefficients. The ability of the coding algorithm to compress ECG signals is investigated, the results were obtained by compressing and decompressing the test signals. The proposed algorithm is compared with direct-based and wavelet-based compression algorithms and showed superior performance. A compression ratio of 24:1 was achieved for MIT-BIH record 117 with a percent root mean square difference as low as 1.08%.
This paper presents an ECG compression algorithm based on wavelet transform and region of interest (ROI) coding. The algorithm has realized near-lossless coding in ROI and quality controllable lossy coding outside of ROI. After mean removal of the original signal, multi-layer orthogonal discrete wavelet transform is performed. Simultaneously,feature extraction is performed on the original signal to find the position of ROI. The coefficients related to the ROI are important coefficients and kept. Otherwise, the energy loss of the transform domain is calculated according to the goal PRDBE (Percentage Root-mean-square Difference with Baseline Eliminated), and then the threshold of the coefficients outside of ROI is determined according to the loss of energy. The important coefficients, which include the coefficients of ROI and the coefficients that are larger than the threshold outside of ROI, are put into a linear quantifier. The map, which records the positions of the important coefficients in the original wavelet coefficients vector, is compressed with a run-length encoder. Huffman coding has been applied to improve the compression ratio. ECG signals taken from the MIT/BIH arrhythmia database are tested, and satisfactory results in terms of clinical information preserving, quality and compress ratio are obtained.
The author proposed an effective wavelet-based ECG compression algorithm (Rajoub, 2002). The reported extraordinary performance motivated us to explore the findings and to use it in our research activity. During the implementation of the proposed algorithm several important points regarding accuracy, methodology, and coding were found to be improperly substantiated. This paper discusses these findings and provides specific subjective and objective measures that could improve the interpretation of compression results in these research-type problems.