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The paper deals with formal description of data transformation (compression and decompression process). We start by briefly reviewing basic concepts of data compression and introducing the model based approach that underlies most modern techniques. Then we present the arithmetic coding and Huffman coding for data compression, and finally see the pe...
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Citations
... DST is used for signal transformation, where transform coefficients are thresholded using bisection algorithm to match the predefined user-specified percentage root mean square difference (UPRD). Huffman coding [3] is used to encode the lookup tables that record the position map of zero and non-zero coefficients (NZCs). The Max-Lloyd quantizer, quantize NZCs followed by Arithmetic coding [4][5][6][7][8][9][10][11][12]. ...
In this work, Blood Pressure (BP) Signal Compression in Salt Sensitive Dahl Rat has been done using Discrete Sine Transform (DST). In order to keep the user specified percentage root mean square difference (UPRD) within the tolerance, first transform coefficients are thresholded by bisection algorithm. The binary look up tables is used to store the position map for zero and non-zero coefficients (NZC). After quantization of NZC, it is compressed with Arithmetic coding is used to encode the quantized NZC. The analysis includes the dissimilar characteristics of blood pressure signals and found that compression ratio is directly proportional to the user define PRD (UPRD).
... Huffman coding [4] is used to encode the lookup tables that record the position map of zero and non-zero coefficients (NZCs). The Max-Lloyd quantizer is used to quantize NZCs followed by Arithmetic coding [5][6][7]. ...
This paper is to show the quality controlled based compression using Fractional Fourier Transform (FRFT) for Blood Pressure Signal Compression in Salt Sensitive Dahl Rat. The methodology used for this work is as first transform coefficients are thresholded by bisection algorithm. It is done to keep the user specified percentage root mean square difference (UPRD) within the tolerance. The position map for zero and non-zero coefficients (NZC) are stored in binary look up tables. Arithmetic coding is used to encode the quantized NZC. For analysis different characteristics of blood pressure signals are consider and found that compression ratio is directly proportional to the user define PRD (UPRD).
... DST is used for signal transformation, where transform coefficients are thresholded using bisection algorithm to match the predefined user-specified percentage root mean square difference (UPRD). Huffman coding [3] is used to encode the lookup tables that record the position map of zero and non-zero coefficients (NZCs). The Max-Lloyd quantizer, quantize NZCs followed by Arithmetic coding [4][5][6][7][8][9][10][11][12]. ...
In this work, Blood Pressure (BP) Signal Compression in Salt Sensitive Dahl
Rat has been done using Discrete Sine Transform (DST). In order to keep the
user specified percentage root mean square difference (UPRD) within the
tolerance, first transform coefficients are thresholded by bisection algorithm.
The binary look up tables is used to store the position map for zero and nonzero
coefficients (NZC). After quantization of NZC, it is compressed with
Arithmetic coding is used to encode the quantized NZC. The analysis includes
the dissimilar characteristics of blood pressure signals and found that
compression ratio is directly proportional to the user define PRD (UPRD).
... Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are the most popular transform techniques [2,3]. Huffman coding and Arithmetic coding are the most popular entropy coding techniques [3,4]. ...
Medical imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Ultrasound (US) produce a large amount of digital medical images. Hence, compression of digital images becomes essential and is very much desired in medical applications to solve both storage and transmission problems. But at the same time, an efficient image compression scheme that reduces the size of medical images without sacrificing diagnostic information is required. This paper proposes a novel threshold-based medical image compression algorithm to reduce the size of the medical image without degradation in the diagnostic information. This algorithm discusses a novel type of thresholding to maximize Compression Ratio (CR) without sacrificing diagnostic information. The compression algorithm is designed to get image with high optimum compression efficiency and also with high fidelity, especially for Peak Signal to Noise Ratio (PSNR) greater than or equal to 36 dB. This value of PSNR is chosen because it has been suggested by previous researchers that medical images, if have PSNR from 30 dB to 50 dB, will retain diagnostic information. The compression algorithm utilizes one-level wavelet decomposition with threshold-based coefficient selection.
... Singla et al. [1] gives the comparative analysis between Huffman and Arithmetic, concluding arithmetic coding is superior to Huffman. As arithmetic accommodates adaptive models easily and provide separation between model and coding. ...
... Huffman Coding was developed by David A. Huffman while he was a Ph.D. student at MIT, and published in the 1952. paper "A Method for the Construction of Minimum-Redundancy Codes" [1]. Huffman coding is an entropy encoding algorithm used for lossless data compression. ...
Data compression is an effective means for saving storage space and channel bandwidth. There are two main types of compression lossy and lossless. This paper will deal with lossless compression techniques named Huffman, Arithmetic, LZ-78 and Golomb coding. The paper attempts to do comparative analysis in terms of their compression efficiency and speed. The test files used for this include English text files, Log files, Sorted word list and geometrically distributed data text file. The implementation results of these compression algorithms suggest the efficient algorithm to be used for a certain type of file to be compressed taking into consideration both the compression ratio and speed of operation. In terms of compression ratios, Golomb is best suited for very low frequency Text files, arithmetic for moderate and high frequency. Implementation is done using MATLAB software.
... From Table 3, and the results of illustrative example 2, showed how efficient and practicable [9] the proposed data compression scheme in Table 1 and decompression algorithms in section 4.2 in handling any kinds of plaintext in an unsecure channel and how it increases storage capacity for faster data processing [3]. ...
... The meaningless results in Table 3 is in line with the proposed data compression and decompression algorithms which helped to conceal the content of sensitive information from the terrorist [9,10]. These meant that the proposed algorithms has proved to prevent the hacker from gaining insight of what the content of information was all about while in either transit or store. ...
This work deals with encoding algorithm that conveys a message that generates a "compressed" form with fewer characters only understood through decoding the encoded data which reconstructs the original message. The proposed factorization techniques in conjunction with lossless method were adopted in compression and decompression of data for exploiting the size of memory, thereby decreasing the cost of the communications. The proposed algorithms shade the data from the eyes of the cryptanalysts during the data storage or transmission.
Due to the power is limited on each node of wireless sensor networks (WSNs), a data compression mechanism is required to achieve the purpose of power saving. In this paper, an efficient data compression method is proposed to reduce the size of transmission data under the given error bound. We first apply the observed transmission data to construct a static Huffman codebook which is related to the data correlation of the monitoring environment. Given an error bound, the proposed method determines whether the new sensed data should be sent or not by comparing it with the reference data such as the previous sensed data (for temporal correlation), the neighboring sensed data (for spatial correlation) and the codebook encoded data (for data correlation). Thus, the total size of transmission data can be minimized for power saving. Simulation results show that the proposed method can make WSNs more efficient in energy consumption. Even the error bound is set as a small value (under 0.009), the proposed method can reduce a lot of the transmission data (over 65%) to cut down the total energy consumption. Comparing to DF-TS, our improvement is nearly 70% in the total energy consumed
