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Huffman Coding of ACIS Pixel Data. Part Number 3. 6- 5. Version 0. 1a. July 2. Massachusetts Institute of Technology. Center for Space Research. Cambridge, Massachusetts 0. Table of Contents. In order to make the best use of the available down- link bandwidth. ACIS 1. 2- bit pixel data may be compressed by the flight software. The. method chosen is known as the . This. memorandum describes the algorithm in some detail, and explains how to. The algorithm was first described by D. Huffman in Proceedings. Institute of Radio Engineers, vol. Soderblom of USGS. Voyager Project. The intent of the algorithm is to translate the input, consisting of. Compression is achieved by. We require that. the mapping be lossless, i. Its. major advantage over the Huffman scheme is that it can adapt to changes. ACIS telemetry. because a loss of part of the compressed string prevents the remainder. If the N integers . Gallager, R. G., Information Theory and Reliable Communication. New York, Wiley, 1. In analogy with statistical mechanics, s. If the pi are identical,i.
Pi ~ 2- mi. with integer mi, and then encodes . The process is. repeated until a single element remains, with p = 1. The process is shown graphically in. The coding tree serves two purposes- -to generate the bit. Eventually, the. path will end at a leaf representing the decoded value. The search then. resumes at the top of the tree. The Huffman tree is not particularly useful when compressing the. This is best done by storing the varying length. Indeed, including it will reduce the overall efficiency of. This would not. however, be acceptable in the present circumstances since we must. ACIS pixels will take on all values within the range 0- 4. The most important function for our Huffman algorithm will be in. Each Front End Processor (FEP) will typically contribute 4. Within. each array, the element values . Huffman Coding: A CS2 Assignment From ASCII Coding to Huffman Coding. Many programming languages use ASCII coding for characters (ASCII stands for American Standard Code for Information Interchange). Some recent languages, e.g.Huffman Encoding/Decoding Program. The program archive comes with no guarantee at all that it will work. The general usage syntax for the Huffman program is. The width of the. CCDs of a. particular type (Front- Illuminated vs. Back- Illuminated), but is. CCDs suffer radiation. Typical values of. AXAF\'s orbit. If we were to compress the raw bias values without allowing for. CCDs, we would. have to use separate trees for each distribution. A particularly simple. Since the bias maps. A change in b. 0 from node to node would therefore. The. ACIS compression algorithm therefore reads as follows: Beginning with the first pixel of the last row of the bias map. This is done so that the bias values. HRMA focus are received first. Within a row, the pixel. CCD, beginning with the first column that would be. A, etc.)If bi is 4. Huffman. string. Otherwise, bi is subtracted from the value of the. Huffman strings. The table has 8. At the start of the process, the . If so, the current. In this manner, each. It consists of a header. The first header element, table. Id. is a 3. 2- bit quantity that identifies this particular table. The second element. Limit, determines the index of the table element that encodes. Use of shorter tables is. The fourth header element. Trunc. Code, is also reserved for truncated tables. The remaining. pair of header elements, huff. Bad. Bias. Code and huff. Bad. Pixel. Code. The string begins at bit 3. L and ends at bit 3. The length is contained in the 5 least- significant bits; the. The longest string that can be. The little- endian. Since the full- length tables are uncomfortably large for the D- cache. RAM available in the ACIS back- end processor, provision has been made. Size < 8. 18. 7. The compression. algorithm is identical to that described in the preceding section. Limit. is tested. If it is less than zero or. Size- 1, the value is not contained in the table. Trunc. Code. string and written to the output uncompressed. The value of low. Limit is 3. 96. 5 (i. Example of a Truncated Compression Table. The pixel value is treated as . This process is. illustrated in Figure 4. The huff. Trunc. Code. This allows the. string, its 5- bit length, and the uncompressed 1. All data compression within the ACIS Back End Processor is handled. Huffman. Table class, each applying a particular. Huffman table (or none at all). The public methods are as follows: Huffman. Table: :Huffman. Table()creates an . The array length is. Size field, i. e. The consequences of this are discussed below. If. icache. Addr is NULL, no table will be loaded and subsequent. Pack. Data will not use Huffman compression. Boolean Huffman. Table: :Pack. Data(const unsigned short *in. Ptr. unsigned & in. Length, unsigned *out. Ptr, unsigned & outlength)compresses an array of in. Length 1. 6- bit words beginning at. Ptr into the output buffer beginning at out. Ptr. It also updates in. Length and outlength. A Huffman. Table object remembers the state of the last. Pack. Data call. The next time its Pack. Data method is. invoked without an intervening reset, it will remember whether. If a Huffman. Table instance is created and its Pack. Data. method is used without any prior Load. Table call, or if the most. Load. Table call specified an icache. Addr of. NULL, no compression will be performed- -the least significant. Huffman. Table: :reset(unsigned last. Num)resets the private variables that remember the state of the most recent. Pack. Data. This is called before creating a new telemetry. It cannot interleave the packets. FEPs since they may require different compression. Within a given science run, all raw mode telemetry must be. Huffman table. The bias. Thief cannot be used during a raw mode science. The huff program. FITS image file and generates a pixel- difference frequency. Huffman tree from the histogramcreates a Huffman table from the treeuses the table to compress the original FITS imagecreates a Huffman tree from the tableuses the tree to decompress the compressed filecompares the result to the original FITS file. The command syntax is. If the byte. order of the pixels doesn\'t match that of the host computer, use the. The Huffman table will contain. The table. Id value will be set to. The compressed. image will be written to out. If the - t option is. Huffman table will be written to the file named. Alternatively, if the - r flag is specified. Huffman table will be read from. Any unfilled histogram. If this were not done, the resulting. After creating the Huffman tree and table, the length of the. If. longer than 1. Since this exchange is somewhat arbitrary, an. This supplies an. The user invokes huff with. Note that this only affects the. To help understand the program, here is a brief description of each. Fits(char *in, unsigned *nx, unsigned. Open the FITS file in, return its size. Hist. From. File(char *infile, int bswap. Read the FITS file infile and return a pointer to its. If bswap is non- zero, the. The number of histogram elements will. NODE *make. Tree. From. Hist(unsigned *hist, unsigned size. Convert the histogram hist into a Huffman tree, returning. The histogram contains size. On entry, only the. HUFFTAB *make. Huff. From. File(char *infile, int bswap. Read the FITS file infile and return the address of its. Huffman compression table. The number of pixel- difference elements will. Tree. From. Table(HUFFTAB *huff)Transform Huffman table huff into a tree, returning a. NODE *add. Leaf. To. Tree(NODE *np, int val, unsigned code. Called recursively from make. Tree. From. Table to locate the node. HUFFTAB *read. Table(char *table)Called to read a Huffman table from the file table. Table(char *outfile, HUFFTAB *huff)Called to write the Huffman table huff to the file. File(char *infile, int bswap, char *outfile. HUFFTAB *huff)Compress the FITS file infile using the Huffman table. Array(unsigned short *in, unsigned inlen. HUFFTAB *huff, unsigned init. Val)Compress inlen elements of pixel array. The first pixel will be. File(char *infile, int bswap, char *tmpfile. HUFFTAB *huff)Decompress the file tmpfile using the Huffman table. Compare each decompressed row with the contents of. FITS image infile. If bswap is non- zero. Array(unsigned *in, unsigned inlen. NODE *root, unsigned offset. Val)Decompress outlen 1. Add initval to the first decompressed value. The - n option specifies. Huffman table is to have only 2. RMS deviation about the mean. It lists the number of counts in the. The next output line lists the length in bits of the most frequent. Huffman string (min), the least frequent (max). Finally, huff reports the size of the compressed file, the. The following. script will read a binary Huffman table and write. It is optimized to compress a bias map with. After the first three header variables, tabid, lowlim. Huffman strings and the third. The numeric values in the first column refer to the. Since the Huffman algorithm typically. Format 2 (ACIS in the focus). The situation is different in raw. Te. Raw. and data. Cc. Raw packets each. It. is therefore necessary to consider two types of damage to packets. Assuming that only one minor frame is missing, the task is to. For. packets, which will usually span no more than two minor frames, the. If this situation arises frequently, it can be. Huffman table such that no string ends with the. Although it is not, in general. Begin by decompressing the remainder of the packet assuming that bit. Huffman string and that the previous. Since any infinite bit string can be. Huffman strings, this process will yield a. Nn pixel values . Some choices of n can. This process should be repeated for. Huffman string. When the packet is a. Bad. Pixel. Code strings, the absence of those strings can be used. The most probable choice among the surviving values of n may. The sum should, of course. For compressed. raw pixel packets, values of bi,n larger than some. Finally, before choosing the smallest pn. This is. easily done by solving the equation. The choice of column weighs. CCD. Typically, the. Unlike the previous case, in which an entire minor frame was. It. will sometimes announce itself, e. When the compression is repeated for. L will vary about this value. For optimal. Huffman codes, the variation in N\' resulting from random bit. Even. if certain pixels are known to be . Since. each element represents a difference of pixel values, the result will. In conclusion, since there is no reliable method of detecting a bit. \n
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