LUISA: Decoupling the Frequency Model From the Context Model in Prediction-Based Compression

2020 ◽  
Author(s):  
Vinicius Fulber-Garcia ◽  
Sérgio Luis Sardi Mergen
Keyword(s):  
Natural Language ◽  
Compression Ratio ◽  
Efficient Algorithm ◽  
Compression Method ◽  
Context Model ◽  
Frequency Model ◽  
Meta Data ◽  
Partial Matching ◽  
Entropy Encoding ◽  
Compressed Data

Abstract Prediction-based compression methods, like prediction by partial matching, achieve a remarkable compression ratio, especially for texts written in natural language. However, they are not efficient in terms of speed. Part of the problem concerns the usage of dynamic entropy encoding, which is considerably slower than the static alternatives. In this paper, we propose a prediction-based compression method that decouples the context model from the frequency model. The separation allows static entropy encoding to be used without a significant overhead in the meta-data embedded in the compressed data. The result is a reasonably efficient algorithm that is particularly suited for small textual files, as the experiments show. We also show it is relatively easy to built strategies designed to handle specific cases, like the compression of files whose symbols are only locally frequent.

scholarly journals Prototipe Kompresi Lossless Audio Codec Menggunakan Entropy Encoding

2010 ◽  
Vol 1 (2) ◽  
pp. 749
Author(s):  
Andreas Soegandi
Keyword(s):  
Compression Ratio ◽  
Lossless Compression ◽  
Compression Method ◽  
File Size ◽  
Audio File ◽  
Audio Codec ◽  
Entropy Encoding

The purpose of this study was to perform lossless compression on the uncompress audio file audio to minimize file size without reducing the quality. The application is developed using the entropy encoding compression method with rice coding technique. For the result, the compression ratio is good enough and easy to be developed because the algorithm is quite simple. 

scholarly journals 3D DCT Based Image Compression Method for the Medical Endoscopic Application

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1817
Author(s):  
Jiawen Xue ◽  
Li Yin ◽  
Zehua Lan ◽  
Mingzhu Long ◽  
Guolin Li ◽  
...  
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Blocking Artifacts ◽  
Compression Method ◽  
High Compression Ratio ◽  
Multiplication Operation ◽  
3D Data ◽  
Wireless Capsule

This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the quantization and zigzag scan are ameliorated. To further improve the visual quality of decompressed images, a frequency-domain filter is proposed to eliminate the blocking artifacts adaptively. Experiments show that our method attains an average compression ratio (CR) of 22.94:1 with the peak signal to noise ratio (PSNR) of 40.73 dB, which outperforms state-of-the-art methods.

scholarly journals Parameter space dimension reduction of an adaptive interpolator during multidimensional signal differential compression

2019 ◽  
pp. 23-30
Author(s):  
A I Maksimov ◽  
M V Gashnikov
Keyword(s):  
Dimension Reduction ◽  
Parameter Space ◽  
Compression Ratio ◽  
Space Dimension ◽  
Computational Experiments ◽  
Parameter Range ◽  
Compression Method ◽  
Multidimensional Signals ◽  
Multidimensional Signal ◽  
Adaptive Interpolator

We propose a new adaptive multidimensional signal interpolator for differential compression tasks. To increase the efficiency of interpolation, we optimize its parameters space by the minimum absolute interpolation error criterion. To reduce the complexity of interpolation optimization, we reduce the dimension of its parameter range. The correspondence between signal samples in a local neighbourhood is parameterized. Besides, we compare several methods for such parameterization. The developed adaptive interpolator is embedded in the differential compression method. Computational experiments on real multidimensional signals confirm that the use of the proposed interpolator can increase the compression ratio.

scholarly journals An Enhanced Run-Length Encoding Compression Method for Telemetry Data

2017 ◽  
Vol 24 (3) ◽  
pp. 551-562 ◽  
Author(s):  
Yanhu Shan ◽  
Yongfeng Ren ◽  
Guoyong Zhen ◽  
Kaiqun Wang
Keyword(s):  
Compression Ratio ◽  
High Capacity ◽  
Acquisition System ◽  
Compression Method ◽  
Telemetry Data ◽  
Vibration Signals ◽  
Run Length ◽  
Compression Performance ◽  
Error Factor ◽  
Method Show

AbstractThe telemetry data are essential in evaluating the performance of aircraft and diagnosing its failures. This work combines the oversampling technology with the run-length encoding compression algorithm with an error factor to further enhance the compression performance of telemetry data in a multichannel acquisition system. Compression of telemetry data is carried out with the use of FPGAs. In the experiments there are used pulse signals and vibration signals. The proposed method is compared with two existing methods. The experimental results indicate that the compression ratio, precision, and distortion degree of the telemetry data are improved significantly compared with those obtained by the existing methods. The implementation and measurement of the proposed telemetry data compression method show its effectiveness when used in a high-precision high-capacity multichannel acquisition system.

scholarly journals A Simple and Efficient IQ Data Compression Method Based on Latency, EVM, and Compression Ratio Analysis

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 117436-117447 ◽  
Author(s):  
Jewon Lee ◽  
Eunhee Hyun ◽  
Joon-Young Jung
Keyword(s):  
Data Compression ◽  
Compression Ratio ◽  
Ratio Analysis ◽  
Compression Method

Application of Texture in a Compressed Visual Simulation

2014 ◽  
Vol 644-650 ◽  
pp. 4261-4264
Author(s):  
Wei Zhang ◽  
Chun Hua Zhang ◽  
Jia Yue Ren
Keyword(s):  
Compression Ratio ◽  
Visual Simulation ◽  
Compression Method ◽  
Texture Memory

Texture compression technology is an effective way to improve the capacity of the texture without increasing the texture memory. This paper studies a texture compression method based on mipmap technology, details several texture compression formats, and describes the texture file of several formats generating and corresponding parameters adjustment. Practical results show that the texture compression method based on mipmap technology can not only achieve high texture compression ratio, but also cause no effect on the texture quality.

scholarly journals On the Randomness of Compressed Data

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 196
Author(s):  
Shmuel T. Klein ◽  
Dana Shapira
Keyword(s):  
Lossless Compression ◽  
Arithmetic Coding ◽  
Huffman Coding ◽  
Compression Method ◽  
Random Data ◽  
Present Evidence ◽  
Compressed Data ◽  
Good Compression

It seems reasonable to expect from a good compression method that its output should not be further compressible, because it should behave essentially like random data. We investigate this premise for a variety of known lossless compression techniques, and find that, surprisingly, there is much variability in the randomness, depending on the chosen method. Arithmetic coding seems to produce perfectly random output, whereas that of Huffman or Ziv-Lempel coding still contains many dependencies. In particular, the output of Huffman coding has already been proven to be random under certain conditions, and we present evidence here that arithmetic coding may produce an output that is identical to that of Huffman.

Multi-Channel ECG Signals Compression Algorithm Using Simultaneous Orthogonal Matching Pursuit

2012 ◽  
Vol 457-458 ◽  
pp. 1305-1309
Author(s):  
Yong Ting Li ◽  
Xiao Yan Chen ◽  
Yue Wen Liu
Keyword(s):  
Signal Processing ◽  
Compression Ratio ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Compression Method ◽  
Petersburg Institute ◽  
Redundant Dictionary ◽  
Compression Performance ◽  
Ecg Signals ◽  
Ecg Compression

Sparse decompression is a new theory for signal processing, having the advantage in that the base (dictionary) used in this theory is over-complete, and can reflect the nature of signa1. So the sparse decompression of signal can get sparse representation, which is very important in data compression. In this paper, a novel ECG compression method for multi-channel ECG signals was introduced based on the Simultaneous Orthogonal Matching Pursuit (S-OMP). The proposed method decomposes multi-channel ECG signals simultaneously into different linear expansions of the same atoms that are selected from a redundant dictionary, which is constructed by Hermite fuctions and Gobar functions in order to the best match the characteristic of the ECG waveform. Compression performance has been tested using a subset of multi-channel ECG records from the St.-Petersburg Institute of Cardiological Technics database, the results demonstrate that much less atoms are selected to present signals and the compression ratio of Multi-channel ECG can achieve better performance in comparison to Simultaneous Matching Pursuit (SMP).

scholarly journals Impact of Complexity and Compression Ratio of Compression Method on Lifetime of Vision Sensor Node

2017 ◽  
Vol 23 (3) ◽  
Author(s):  
Khursheed Aurangzeb ◽  
Musaed Alhussein ◽  
Syed Irtaza Haider
Keyword(s):  
Compression Ratio ◽  
Sensor Node ◽  
Vision Sensor ◽  
Compression Method

scholarly journals New Lossless Compression Method using Cyclic Reversible Low Contrast Mapping (CRLCM)

2016 ◽  
Vol 6 (6) ◽  
pp. 2836
Author(s):  
Hendra Mesra ◽  
Handayani Tjandrasa ◽  
Chastine Fatichah
Keyword(s):  
Compression Ratio ◽  
Image Data ◽  
Lossless Compression ◽  
Huffman Coding ◽  
Compression Method ◽  
Low Contrast ◽  
Coding Method

<p>In general, the compression method is developed to reduce the redundancy of data. This study uses a different approach to embed some bits of datum in image data into other datum using a Reversible Low Contrast Mapping (RLCM) transformation. Besides using the RLCM for embedding, this method also applies the properties of RLCM to compress the datum before it is embedded. In its algorithm, the proposed method engages Queue and Recursive Indexing. The algorithm encodes the data in a cyclic manner. In contrast to RLCM, the proposed method is a coding method as Huffman coding. This research uses publicly available image data to examine the proposed method. For all testing images, the proposed method has higher compression ratio than the Huffman coding.</p>

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