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  2. UTF-16 - Wikipedia

    en.wikipedia.org/wiki/UTF-16

    The Joliet file system, used in CD-ROM media, encodes file names using UCS-2BE (up to sixty-four Unicode characters per file name). Python version 2.0 officially only used UCS-2 internally, but the UTF-8 decoder to "Unicode" produced correct UTF-16. There was also the ability to compile Python so that it used UTF-32 internally, this was ...

  3. Run-length encoding - Wikipedia

    en.wikipedia.org/wiki/Run-length_encoding

    Even binary data files can be compressed with this method; file format specifications often dictate repeated bytes in files as padding space. However, newer compression methods such as DEFLATE often use LZ77 -based algorithms, a generalization of run-length encoding that can take advantage of runs of strings of characters (such as BWWBWWBWWBWW ).

  4. Comparison of Unicode encodings - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_Unicode...

    The tables below list the number of bytes per code point for different Unicode ranges. Any additional comments needed are included in the table. The figures assume that overheads at the start and end of the block of text are negligible. N.B. The tables below list numbers of bytes per code point, not per user visible "character" (or "grapheme ...

  5. Wide character - Wikipedia

    en.wikipedia.org/wiki/Wide_character

    A wide character refers to the size of the datatype in memory. It does not state how each value in a character set is defined. Those values are instead defined using character sets, with UCS and Unicode simply being two common character sets that encode more characters than an 8-bit wide numeric value (255 total) would allow.

  6. UTF-32 - Wikipedia

    en.wikipedia.org/wiki/UTF-32

    UTF-32 (32-bit Unicode Transformation Format), sometimes called UCS-4, is a fixed-length encoding used to encode Unicode code points that uses exactly 32 bits (four bytes) per code point (but a number of leading bits must be zero as there are far fewer than 2 32 Unicode code points, needing actually only 21 bits). [1]

  7. Huffman coding - Wikipedia

    en.wikipedia.org/wiki/Huffman_coding

    Huffman tree generated from the exact frequencies of the text "this is an example of a huffman tree". Encoding the sentence with this code requires 135 (or 147) bits, as opposed to 288 (or 180) bits if 36 characters of 8 (or 5) bits were used (This assumes that the code tree structure is known to the decoder and thus does not need to be counted as part of the transmitted information).

  8. Hash function - Wikipedia

    en.wikipedia.org/wiki/Hash_function

    A standard technique is to use a modulo function on the key, by selecting a divisor M which is a prime number close to the table size, so h(K) ≡ K (mod M). The table size is usually a power of 2. This gives a distribution from {0, M − 1}. This gives good results over a large number of key sets.

  9. UTF-8 - Wikipedia

    en.wikipedia.org/wiki/UTF-8

    It was designed for backward compatibility with ASCII: the first 128 characters of Unicode, which correspond one-to-one with ASCII, are encoded using a single byte with the same binary value as ASCII, so that a UTF-8-encoded file using only those characters is identical to an ASCII file.