Aproximace statistických kompresních metod pro zlepšení Lazy Evaluation

Abstract

This thesis deals with the improvement of the Lazy Evaluation method with possibilities of integrating static compression in the sense of bettering the compression ratio. Theoretical part of the work focuses on the principle and description of algorithms, which are used in implementation and testing. The stress is laid on the LZSS method with the use of lazy evaluation and Huffman coding and its optimized version. Practical part of the work applies solution proposal with the help of the C# language in the Visual Studio environment. The testing takes place over the testing files from The Canterbury Corpus and The Calgary Corpus collections and verifies the efficiency of the projected LZSS Huffman optimal algorithm. The gained knowledge is then interpreted by compression results charts and tables of comparison of the individual methods over various files.