1/9/2024 0 Comments Lzw decompressor downloadIn this way both sender and receiver are kept in sync yet do not need to propagate these changes in the dictionary to one another because they are inherent to the algorithm! As S builds its dictionary and sends further symbols to R, R will not only add the received symbols to its output, but will also build up a "mirrored dictionary" that is identical to the one S has already built. ![]() LZW relies on the fact that both S and R keep a dictionary of strings that they have already seen in the transmission. So let's get started on how LZW works: let's call the person compressing and then sending the compressed data, 'S', and the receiver to decompress the message 'R'. In my implementation I'm just going to represent the compressed data as a list of integers for simplicity, but it's easy to imagine a smaller symbol type for large compressions. Getting into the mechanics, like Huffman encoding, LZW is a variable length-encoding, meaning that transmitted symbols can be of different sizes. ![]() Moreover, this information is known ahead of time, before any data has even been generated. ![]() The single biggest advantage of LZW is that unlike the traditional Huffman Encoding that was discussed in class, very little information needs to be transmitted from the compressor to the decompressor. As we talked about in our networking class, LZW compression is about as close as you can get to an optimal compression rate for large compression objects. All right, as promised last week today's topic is LZW compression.
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