LZW

(redirected from dictionary method)

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 ?
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.
Adaptive parameter tuning is enabled in this framework within each local patch, which enhances the performance of the original self learning 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.
I wanted to measure the efficacy of this computer-based method against or in relation to the traditional book and dictionary method," Professor Baldini said, "so I tested several groups of students over a two-year period.
This webinar will offer examples of Huffman coding and dictionary methods that fuel the popular dominant methods of Lemple and Ziv.

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