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The possibility of applying compressed matching in JPEG encoded images is investigated and the problems raised by the scheme are discussed. A part of the problems can be solved by the use of some auxiliary data which yields various time/space trade-offs. Finally, approaches to deal with extensions such as allowing scaling or rotations are suggested.

In this paper we propose an efficient approach to the compressed string matching problem on Huffman encoded texts, based on the BOYER-MOORE strategy. Once a candidate valid shift has been located, a subsequent verification phase checks whether the shift is codeword aligned by taking advantage of the skeleton tree data structure. Our approach leads to algorithms that exhibit a sublinear behavior on the average, as shown by extensive experimentation.

This work proposes a lossless data compression algorithm for short data blocks. The proposed compression scheme combines a modified move-to-front algorithm with Huffman coding. This algorithm is applicable in storage systems where the data compression is performed on block level with short block sizes, in particular, in non-volatile memories. For block sizes in the range of 1kB, it provides a compression gain comparable to the Lempel–Ziv–Welch algorithm. Moreover, encoder and decoder architectures are proposed that have low memory requirements and provide fast data encoding and decoding.

In this paper, source coding or data compression is viewed as a measurement problem. Given a measurement device with fewer states than the observable of a stochastic source, how can one capture their essential information? We propose modeling stochastic sources as piecewise-linear discrete chaotic dynamical systems known as Generalized Luröth Series (GLS) which has its roots in Georg Cantor's work in 1869. These GLS are special maps with the property that their Lyapunov exponent is equal to the Shannon's entropy of the source (up to a constant of proportionality). By successively approximating the source with GLS having fewer states (with the nearest Lyapunov exponent), we derive a binary coding algorithm which turns out to be a rediscovery of Huffman coding, the popular lossless compression algorithm used in the JPEG international standard for still image compression.

Unsolicited bulk e-mail, also known as spam, has been an increasing problem for the e-mail society. This paper presents a new spam filtering strategy that 1) uses a practical entropy coding technique, Huffman coding, to dynamically encode the feature space of the e-mail collected over time and, 2) applies an online algorithm to adaptively enhance the learned spam concept as new e-mail data becomes available. The contributions of this work include a highly efficient spam filtering algorithm in which the input space is radically reduced to a single-dimension input vector, and an adaptive learning technique that is robust to vocabulary change, concept drifting and skewed class distributions. We compare our technique with several existing off-line learning techniques including support vector machine, logistic regression, naïve Bayes, k-nearest neighbor, C4.5 decision tree, RBFNetwork, boosted decision tree and stacking. We demonstrate the effectiveness of our technique by presenting the experimental results on the e-mail data that is publicly available. A more in-depth statistical analysis on the experimental results is also presented and discussed.

Document image compression is important in modern communication systems, and many lossless/lossy compression algorithms have been proposed for a variety of documents. In documents with Chinese characters, there are 5401 commonly used Chinese character patterns. It requires a large amount of space to store all Chinese character patterns of different fonts in computer systems. However, the compression of Chinese character patterns has not been extensively studied. In this paper, a new document image compression method is proposed. The purpose is to provide an effective encoding algorithm for Chinese character patterns and in the meantime obtain good compression results for general documents. The proposed method includes rectangular region partitioning, encoding of rectangular regions, and encoding of contour information. An input image is first partitioned into nonoverlapping blocks, and each block that contains black pixels is partitioned into nonoverlapping rectangles. The rectangles are then encoded in an effective fashion. For the purpose of lossless compression, contour information is exploited to encode the contour blocks with static Huffman coding. Experimental results showed that the proposed method is not only suitable for Chinese documents but also has good performance for general documents.

We have seen that Huffman coding has been widely used in data, image, and video compression. In this paper novel maximal prefix coding is introduced. Relationship between the Huffman coding and the optimal maximal prefix coding are discussed. We show that all Huffman coding schemes are optimal maximal prefix coding schemes and that conversely the optimal maximal prefix coding schemes need not be the Huffman coding schemes. Moreover, it is proven that, for any maximal prefix code C, there exists an information source I = (∑ P) such that C is exactly a Huffman code for I. Therefore, it is essential to show that the class of Huffman codes is coincident with one of the maximal prefix codes. A case study of data compression is also given. Comparing the Huffman coding, the maximal prefix coding is used not only for statistical modeling but also for dictionary methods. It is also good at applying to a large information retrieval system and improving its security.

Each and every business enterprises require noise-free and clean data. There is a chance of an increase in dirty data as the data warehouse loads and refreshes a large quantity of data continuously from the various sources. Hence, in order to avoid the wrong conclusions, the data cleaning process becomes a vital one in various data-connected projects. This paper made an effort to introduce a novel data cleaning technique for the effective removal of dirty data. This process involves the following two steps: (i) dirty data detection and (ii) dirty data cleaning. The dirty data detection process has been assigned with the following process namely, data normalization, hashing, clustering, and finding the suspected data. In the clustering process, the optimal selection of centroid is the promising one and is carried out by employing the optimization concept. After the finishing of dirty data prediction, the subsequent process: dirty data cleaning begins to activate. The cleaning process also assigns with some processes namely, the leveling process, Huffman coding, and cleaning the suspected data. The cleaning of suspected data is performed based on the optimization concept. Hence, for solving all optimization problems, a new hybridized algorithm is proposed, the so-called Firefly Update Enabled Rider Optimization Algorithm (FU-ROA), which is the hybridization of the Rider Optimization Algorithm (ROA) and Firefly (FF) algorithm is introduced. To the end, the analysis of the performance of the implanted data cleaning method is scrutinized over the other traditional methods like Particle Swarm Optimization (PSO), FF, Grey Wolf Optimizer (GWO), and ROA in terms of their positive and negative measures. From the result, it can be observed that for iteration 12, the performance of the proposed FU-ROA model for test case 1 on was 0.013%, 0.7%, 0.64%, and 0.29% better than the extant PSO, FF, GWO, and ROA models, respectively.

The objective of image compression is to reduce the redundant amount of data and to achieve low bit rate without any apparent loss of image quality. In this paper, the compression process is achieved by using wavelet transform with lifting technique for decomposition and tree-structured vector scheme for quantization. Huffman coding method is used for encoding stage. Wavelet technique provides the most promising tool for high-quality image compression. Lifting transform provides a flexible tool for the construction of sub-band decomposition and perfect reconstruction. TSVQ has its advantages over conventional vector quantization by minimizing the computational complexity. Huffman coding is used for efficient coding of images at lower bit rate with minimal loss of information. The performance of the proposed work has been assessed with parameters like bits per pixel (bpp), compression ratio (CR), peak signal to noise ratio (PSNR), codebook size (CS), and mean squared error (MSE). The experimental results show that the proposed work yields a higher compression with better quality reconstructed image than the other conventional compression methods.