LZW

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LZW

(Lempel-Ziv-Welch) A data compression method that stems from techniques introduced by Jacob Ziv and Abraham Lempel. LZW has been used in many hardware and software products, including V.42bis modems, GIF, TIF and PDF files and PostScript Level 2. The LZ77 method creates pointers back to repeating data, while LZ78 creates a dictionary of repeating phrases with pointers to those phrases.

The Welch in Lempel-Ziv-Welch is for Terry Welch, the Unisys researcher who created an enhanced version of these methods that was patented by Unisys. Unisys collected royalties from thousands of software publishers whose applications used this algorithm until the patents expired in 2004. This was the most widely licensed data compression patent in history. See PNG.
References in periodicals archive ?
The tendency classification can achieve a good classification effect whether it is based on the dictionary method or statistical learning method [3].
(1) Each sentence of the Tibetan text is used to determine the sentence's emotions based on dictionary method.
I use a dictionary method to study the sentiment in debates.
(9) Wordscores, used in Section 2, is itself a dictionary method. The dictionary is developed from the word stems in the reference texts, each with its own conservatism score.
This paper is structured as--Section II: Background work with reference to image denoising using Dictionary Method is reviewed, Section III: Covers proposed method to compare the performance of different denoising methods for evaluation, Section IV: Describes experimental result analysis of evaluated methods both quantitatively and qualitatively and Section V: Concludes with providing a suitable solution for image denoising using Self Learning Adaptive Dictionary method.
An dictionary learning scheme is adaptive by joining the advantage of DCT and Global dictionary method.
As compared to DCT and Global Dictionary method, the experimental result shows that SLAD method is much effective for the process of image denoising.
In this study, robot arm's joint angles were determined using the coordinate dictionary method. Performances of the gradient descent and the coordinate dictionary algorithms are compared in Table V in terms of Euclidian distance error and time consumed while finding the best solution for the objects.
Results also show that the coordinate dictionary method is much faster than the gradient descent method in which the joint angles are calculated in 30.202 milliseconds.
As the results demonstrate, the proposed fixed dictionary method outperforms LEC, always achieving a larger compression ratio.
The novelty of this paper is that it combines dictionary methods, i.e.
Another is to train a dictionary from a large number of training image patches, like K-SVD [32], which usually shows better performance in image processing compared to the fixed dictionary methods [11].

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