of speed is the number of bits it can process at a time. In this respect, compression methods like LZ78 most widely known through its variant LZW, have the distinct advantage of being able to read an entire input word at a time, as encoded words have the same length. However, the same technique has the disadvantage of having to dynamically generate and maintain the contents of the dictionary.
A solution that targets both speed and simplicity would be to use a statistical dictionary that is computed based on the contents of the entire bitstream and is used throughout the entire decompression. Unlike the Huffman dictionary, there is no clear
methodology for how such a dictionary can be created in an optimal way (at least not to the knowledge of the authors), but the characteristics of the bitstream make the choice an easy one. In particular, due to the high probability of occurrence of the zero symbol, the coding scheme degenerates into a bit-level RLE with minor modifications.
压缩位掩码-4. BITMASK- BASED COMPRESSION
Bitmask-based compression is an enhancement on the dictionary-based compression scheme, that helps us to get more matching patterns. In dictionary-based compression, each vector is compressed only if it completely matches with a dictionary entry.
As seen in Fig. 4, we can compress up to six data entries using bitmask based compression. The compressed data is represented as follows. Those vectors that match directly are compressed with 3 bits. The first bit represents whether it is compressed (using 0) or not (using 1). The second bit indicates whether it is compressed using bitmask (using 0) or not (using 1). The last bit indicates the dictionary index. Data that are compressed using bitmask requires 7 bits. The first two bits, as before, represent if the data is compressed, and whether the data is compressed using bitmasks. The next two bits indicate the bitmask position and followed by two bits that indicate the bitmask pattern.
For example, the last data vector in Fig. 4 is compressed using a bitmask. The bitmask position is 11, which indicates the fourth even bit position from left. For this case, we have assumed fixed bitmasks, which are always employed on even-bit positions and hence only 2 bits are sufficient to represent the four positions in a 8-bit data. The last bit gives the dictionary index. The bitmask XORed with the dictionary entry produces the original data. In this example, the compression efficiency is 27.5%, based on the following formula expressed as percentage:
Comp. Efficiency = Original Size -Compressed Size
Original Size
Since existing approach does not handle don't cares ('X'), in this example we have replaced all don't cares by 1. Note that we could have replaced all don't cares with 0's as well. In that case, it will result in worse compression efficiency of 2.5%. A better compression efficiency can be achieved by selectively replacing the don't cares with '0' or '1' instead of replacing all by 0's (or 1's). It is a major challenge to identify the selective replacement to generate the best possible compression efficiency.
运行长度编码压缩的单词-5. RUN LENGTH ENCODING OF COMPRESSED WORDS
The configuration bitstream usually contains consecutive repeating bit sequences. Although the bitmask-based compression encodes such patterns using same
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