Universal Compression Algorithm Research Articles

Development of an algorithm for compressing aerospace images for the subsequent recognition and identification of various objects

The object of study is the recognition and identification of various objects in aerospace images. To solve the problems of compressing hyperspectral aerospace images with losses, the development of a compression algorithm is proposed. As a result, an algorithm has been developed for compressing aerospace images for subsequent recognition and identification of various objects using wavelet transform for processing high- and medium-resolution space images when monitoring from remote sensing satellites, based on the use of structural features of object images. In particular, orthogonal and wavelet transforms are presented, adapted for compression of hyperspectral aerospace images with losses, an adaptive discrete cosine transform algorithm is presented, followed by quantization with a loss level and compression. Thanks to a series of experiments on hyperspectral aerospace images, the effectiveness of the proposed algorithm in terms of the degree of compression, as well as the characteristics of the limits of its applicability, can be highlighted. The use of wavelets provides progressive compression of the bitstream, which makes it possible to achieve lossless compression with minimal loss of information due to the modified Huffman algorithm with a compression ratio of 9 more than 2.5 times in existing algorithms, as well as the quality metric of the restored images, the peak signal-to-noise ratio is sufficiently below 32.56. The developed compression algorithm demonstrates the effectiveness of its application in terms of a set of characteristics and is superior to analogues. The scope and conditions for the practical use of the results obtained is a comparison of the proposed algorithm with the results of experiments obtained for universal compression algorithms for archivers and a compressor

Universal Lossless Compression of Graphical Data

Graphical data is comprised of a graph with marks on its edges and vertices. The mark indicates the value of some attribute associated to the respective edge or vertex. Examples of such data arise in social networks, molecular and systems biology, and web graphs, as well as in several other application areas. Our goal is to design schemes that can efficiently compress such graphical data without making assumptions about its stochastic properties. Namely, we wish to develop a universal compression algorithm for graphical data sources. To formalize this goal, we employ the framework of local weak convergence, also called the objective method, which provides a technique to think of a marked graph as a kind of stationary stochastic processes, stationary with respect to movement between vertices of the graph. In recent work, we have generalized a notion of entropy for unmarked graphs in this framework, due to Bordenave and Caputo, to the case of marked graphs. We use this notion to evaluate the efficiency of a compression scheme. The lossless compression scheme we propose in this paper is then proved to be universally optimal in a precise technical sense. It is also capable of performing local data queries in the compressed form.

DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks

In the past decade deep neural networks (DNNs) have shown state-of-the-art performance on a wide range of complex machine learning tasks. Many of these results have been achieved while growing the size of DNNs, creating a demand for efficient compression and transmission of them. In this work we present DeepCABAC, a universal compression algorithm for DNNs that is based on applying Context-based Adaptive Binary Arithmetic Coder (CABAC) to the DNN parameters. CABAC was originally designed for the H.264/AVC video coding standard and became the state-of-the-art for the lossless compression part of video compression. DeepCABAC applies a novel quantization scheme that minimizes a rate-distortion function while simultaneously taking the impact of quantization to the DNN performance into account. Experimental results show that DeepCABAC consistently attains higher compression rates than previously proposed coding techniques for DNN compression. For instance, it is able to compress the VGG16 ImageNet model by x63.6 with no loss of accuracy, thus being able to represent the entire network with merely 9 MB. The source code for encoding and decoding can be found at https://github.com/fraunhoferhhi/DeepCABAC.

Electricity theft detection framework based on universal prediction algorithm

<span lang="EN-GB">Electricity theft has caused huge losses over the globe and the trend of its perpetuation constantly evolve even as smart technologies such as smart meters are being deployed. Although the smart meters have come under some attacks, they provide sufficient data which can be analysed by an intelligent strategy for effective monitoring and detection of compromised situations. So many techniques have been employed but satisfactory result is yet to be obtained for a real-time detection of this electrical fraud. This work suggests a framework based on Universal Anomaly Detection (UAD) utilizing Lempel-Ziv universal compression algorithm, aimed at achieving a real-time detection in a smart grid environment. A number of the network parameters can be monitored to detect anomalies, but this framework monitors the energy <a name="_Hlk725881"></a>consumption data, rate of change of the energy consumption data, its date stamp and time signatures. To classify the data based on normal and abnormal behaviour, Lempel-Ziv algorithm is used to assign probability of occurrence to the compressed data of the monitored parameters. This framework can learn normal behaviours of smart meter data and give alerts during any detected anomaly based on deviation from this probability. A forced aggressivemeasure is also suggested in the framework as means of applying fines to fraudulent customers.</span>

A universal compression strategy using sorting transformation

Although traditional universal compression algorithms can effectively utilise repetition located in a slide window, they cannot take their own advantages for some message source in which similar messages are distributed uniformly. In this paper, we come up with a universal segmenting-sorting compression algorithm to solve this problem. The key idea is to reorder the message source before compressing it with Lz77 algorithm. We design transformation methods for two common data types, corpus of webpages and access log. The experimental results show that segmenting-sorting transformation is truly beneficial to compression ratio. Our new algorithm is able to make compression ratio 20% to 50% lower than naive Lz77 algorithm does and takes almost the same decompression time. For some read-heavy source segmenting-sorting compression can reduce space cost while guaranteeing throughput.

A universal compression strategy using sorting transformation

Although traditional universal compression algorithms can effectively utilise repetition located in a slide window, they cannot take their own advantages for some message source in which similar messages are distributed uniformly. In this paper, we come up with a universal segmenting-sorting compression algorithm to solve this problem. The key idea is to reorder the message source before compressing it with Lz77 algorithm. We design transformation methods for two common data types, corpus of webpages and access log. The experimental results show that segmenting-sorting transformation is truly beneficial to compression ratio. Our new algorithm is able to make compression ratio 20% to 50% lower than naive Lz77 algorithm does and takes almost the same decompression time. For some read-heavy source segmenting-sorting compression can reduce space cost while guaranteeing throughput.

A Practical, Accurate, Information Criterion for Nth Order Markov Processes

The recent increase in the breath of computational methodologies has been matched with a corresponding increase in the difficulty of comparing the relative explanatory power of models from different methodological lineages. In order to help address this problem a Markovian information criterion (MIC) is developed that is analogous to the Akaike information criterion (AIC) in its theoretical derivation and yet can be applied to any model able to generate simulated or predicted data, regardless of its methodology. Both the AIC and proposed MIC rely on the Kullback–Leibler (KL) distance between model predictions and real data as a measure of prediction accuracy. Instead of using the maximum likelihood approach like the AIC, the proposed MIC relies instead on the literal interpretation of the KL distance as the inefficiency of compressing real data using modelled probabilities, and therefore uses the output of a universal compression algorithm to obtain an estimate of the KL distance. Several Monte Carlo tests are carried out in order to (a) confirm the performance of the algorithm and (b) evaluate the ability of the MIC to identify the true data-generating process from a set of alternative models.

Development of an algorithm for adaptive compression of indexed images using contextual simulation

An algorithm is proposed for compression of indexed raster images (IRI) based on statistical coding using context simulation. A model and methods are developed in order to create an effective algorithm for compression of indexed graphic information. A detailed consideration is given to methods for increasing the compression ratio. The optimization of the algorithm, including computation parallelization, is presented. The proposed algorithm is compared with other universal and specialized compression algorithms.

Universal Multiterminal Source Coding Algorithms With Asymptotically Zero Feedback: Fixed Database Case

Consider a source network in which a finite alphabet source X = {Xi}i=0 infin is to be encoded and transmitted, and another finite alphabet source Y = {Xi}i=0 infin correlated with X is available only to the decoder as side information. Traditionally, the channel between the encoder and decoder in the source network is assumed to be one-way. This, together with the fact that the encoder does not have access to Y, implies that the encoder has to know the achievable rates before encoding. In this paper, we consider universal source coding for a feedback source network in which the channel between the encoder and decoder is two-way and asymmetric. Assume that the encoder and decoder share a random database that is independent of both X and Y. A string matching-based (variable-rate) block coding algorithm with simple progressive encoding and joint typicality decoding is first proposed for the feedback source network. The simple progressive encoder does not need to know the achievable rates at the beginning of encoding. It is proven that for any (X, Y) in a large class of sources satisfying some mixing conditions, the average number of bits per letter transmitted from the encoder to the decoder (compression rate) goes to the conditional entropy H(X | Y) of X given Y asymptotically, and at the same time the average number of bits per letter transmitted from the decoder to the encoder (feedback rate) goes to 0 asymptotically. The algorithm and the corresponding analysis results are then extended to the case where both X and Y are to be encoded separately, but decoded jointly. Finally, a universal decoding algorithm is proposed to replace the joint typicality decoding, and the resulting universal compression algorithm consisting of the simple progressive encoder and the universal decoding algorithm is further shown to be asymptotically optimal for the class of all jointly memoryless source-side information pairs (X,Y).

Measuring the Efficiency of the Intraday Forex Market with a Universal Data Compression Algorithm

Universal compression algorithms can detect recurring patterns in any type of temporal data--including financial data--for the purpose of compression. The universal algorithms actually find a model of the data that can be used for either compression or prediction. We present a universal Variable Order Markov (VOM) model and use it to test the weak form of the Efficient Market Hypothesis (EMH). The EMH is tested for 12 pairs of international intra-day currency exchange rates for one year series of 1, 5, 10, 15, 20, 25 and 30 min. Statistically significant compression is detected in all the time-series and the high frequency series are also predictable above random. However, the predictability of the model is not sufficient to generate a profitable trading strategy, thus, Forex market turns out to be efficient, at least most of the time.

Problems of Robustness for Universal Coding Schemes

The Lempel–Ziv universal coding scheme is asymptotically optimal for the class of all stationary ergodic sources. A problem of robustness of this property under small violations of ergodicity is studied. The notion of deficiency of algorithmic randomness is used as a measure of disagreement between data sequence and probability measure. We prove that universal compression schemes from a large class are nonrobust in the following sense: If the randomness deficiency grows arbitrarily slowly on initial fragments of an infinite sequence, then the property of asymptotic optimality of any universal compression algorithm can be violated. Lempel–Ziv compression algorithms are robust on infinite sequences generated by ergodic Markov chains when the randomness deficiency of their initial fragments of length n grows as o(n).

Branch prediction based on universal data compression algorithms

Data compression and prediction are closely related. Thus prediction methods based on data compression algorithms have been suggested for the branch prediction problem. In this work we consider two universal compression algorithms: prediction by partial matching (PPM), and a recently developed method, context tree weighting (CTW). We describe the prediction algorithms induced by these methods. We also suggest adaptive algorithms --- variations of the basic methods that attempt to fit limited memory constraints and to match the non-stationary nature of the branch sequence. Furthermore, we show how to incorporate address information and to combine other relevant data. Finally, we present simulation results for selected programs from the SPECint95, SYSmark/32, SYSmark/NT, and transactional processing benchmarks. Our results are most promising in programs with difficult to predict branch behavior.