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smallest possible number of bits (almost always 9) When the number of codes becomes too large to be represented by the current code size, the code size is increased by 1 bit If the initial code size is 9, codes are output using 9 bits until code 512 is created Then the code size is automatically bumped up to 10 bits Likewise, after reaching 1024 it is increased to 11 bits Twelve bits is the maximum code size allowed by GIF When the code values reach 212-1, GIF encoders and decoders stop adding to the dictionary A GIF encoder can output a special clear code to instruct the decoder to reset the dictionary to its initial state An encoder may wish to output a clear code whenever it detects a situation where compression would be improved by using a new dictionary An bvious situation where this might be desirable is after the maximum code sized is reached Repeated clear codes are legal, but wasteful The value for the clear code is not fixed, but rather depends upon the minimum code size that followed the image header The minimum code size gives the number of bits used to encode the pixel data Usually this value is 8 bits per pixel, but pixel values for images that do not use 256 colors can be compressed using fewer bits Table 1211 shows the relationship of the minimum code size to the code usage within a GIF image The end code is used to mark the end of a compressed stream Dictionary Structure In the GIF compression and decompression examples we used an array of strings to represent the dictionary Normally the dictionary is represented as a tree structure Figure 127 shows how the strings generated from compressing the string ABABCABD can be arranged in a tree structure A code can be translated to a string by starting at the tree node for the code and working to the tree root Translating the code by walking the tree from a leaf to the root produces the string in reverse order In Figure 127, following the code 260 gives "DBA" A stack is used to put the string in the correct order While walking the tree, each time a leaf node is reached, the corresponding character is pushed on the stack When you reach the root of the tree, you pop characters off in the correct order The tree can be represented as an array of structures Since the maximum code length is 12 bits, the maximum number of tree nodes is 212 The size of the stack is also 212 characters.

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erences the sequence in the dictionary The longer the dictionary sequences are and the more frequently they are repeated in the uncompressed data, the greater the compression The trick in dictionary-based compression is how to transmit the dictionary in the compressed data The most common dictionary-based compression schemes in use are based upon those described by Abraham Lempel and Jacob Ziv (1977 and 1978), and known as LZ77 and LZ78, respectively LZ77 uses a sliding window into the uncompressed data to implement the dictionary1 LZ78 builds a dictionary dynamically from the uncompressed data GIF Compression LZW is a variant of the LZ78 process that was described in a paper by Terry Welsh of Sperry (now Unisys) in 1984 CompuServe adopted it for use in the GIF format shortly afterwards In the LZW method, the compressed data stream consists entirely of codes that identify strings in a dictionary The dictionary is initialized so that it contains each possible data value as a predefined string For example, if 8-bit data is being encoded, the dictionary initially contains 256 1-byte strings containing the values 0-255 The compression reads characters from the input stream and appends them to the current string until the current string no longer has a match in the dictionary At that point it outputs the code for the longest matching string, adds the nonmatching string to the dictionary (the matching string plus one character), and finally starts a new string that contains the first nonmatching character Each time a code is written to the output stream, an entry is added to the dictionary Algorithm 122 illustrates how the dictionary is created in the LZW process Here we are assuming that 8-bit data is being used and that the function Output writes 9-bit codes to the output stream Figure 125 shows how a sample string would be compressed using the LZW process The first column shows the string at each stage in the compression process; the second column shows the value written to the output stream at each stage; and the third column shows the new code that is reated This input string in Figure 125 consists of 27 characters Using 8 bits per character, the string requires 216 bits The LZW process uses 20 literal values and dictionary codes to represent the string, so 9 bits are required to encode each LZW value, giving a total of 180 bits a reduction of 17% in the compressed version It takes only 33 codes to encode the same string repeated twice (a 31% reduction) and 42 codes to encode it repeated three times (a 41% reduction) The more repetition in the input stream, the greater the compression the LZW process gives.

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