To create the programs tsvq, tsvqe, prune, select , and voronoi_tstype "make all" To remove unnecessary files, type "make clean"To change the type of data that the programs will process, change thedefinition of DATA in the file vq.h.PROGRAM    tsvq.cSYNOPSIS    tsvq -t trainingsequence -c codebook -s statistics          -d dimension -r rate -m offset -h threshold -BDESCRIPTION    tsvq forms an unbalanced tree structured VQ codebook. Use the -B option to    create a balanced tree structured VQ codebook.  The input should be one    training sequence that has already been blocked.  Merge multiple training    sequences into one file.  Blocking programs are in the stdvq directory.    The output consists of two files.  One is a codebook file which has the    format:        TYPE       SIZE          DESCRIPTION        long       1             number of nodes in the tree (numnodes)        integer    1             vector dimension (dim)        short      numnodes      tree description array        DISTTYPE   numnodes*dim  codewords    The tree description array is a preorder list where 0 indicates that    a node is terminal and 1 indicates that a node is not terminal.  The    second output file is also a preorder list with two values per tree    node where 1 long is used for the count and 1 DISTTYPE is used for    the distortion.    This program should produce any tree from (rate 0, highest distortion)    to (highest rate, 0 distortion), where the rate is the average number of    bits per vector and the distortion the the average mean squared error per    vector.  To do this, the lloyd subroutine will try two different methods    to split a cell.  The first attempted split is to use the centroid and a    slightly perturbed version of the centroid.  Should this fail, then a    second attempt to split is made by using the centroid and the vector that    is the furthest distance from the centroid. To create different trees,    try playing with the mult_offset parameter.  The minimum number of training    vectors that must be present in order for a codeword to be split is    controlled by DEF_min_vectors in the tsvq.h file.  I do not recommend    playing with the threshold parameter (defaulted to zero, but it can    take values up to 1) since a non-zero threshold violates the local    optimality condition that is assumed by the tree growing algorithm.    This can lead to empty cells on the tree and wasted bits. See    tsvq_util.c under lloyd for more details about the possible side    effects.  This code does not try to correct any of the side effects    caused by using a non-zero threshold, but it does detect empty cells    and prints a warning to the user.  In addition, tsvq prints the following    information to stdout:  rate and distortion as the tree grows, the number    of nodes in the tree, and the empirical entropy of the codewords.    Type "tsvq" to see default values.PROGRAM    tsvqe.cSYNOPSIS    tsvqe -c codebook -i input -o output -R -DDESCRIPTION    tsvqe encodes a blocked input file using a tree-structured VQ codebook    created by tsvq. The inputs are the input image and the codebook.    The output is the decoded image, not the codewords themselves.  There    are two optional outputs.  An instantaneous rate image can be created    by choosing the -R flag, and the counts and distortions can be written    using the -D flag.  The counts and distortions file has the same format    as the statistics file produced by tsvq.c.  In addition, tsvqe prints    the following information to stdout: average rate per vector, average    distortion per vector, entropy of the vectors, and maximum codeword    length.  Type "tsvqe" to see default values.PROGRAM    prune.cSYNOPSIS    prune -c codebook -s statfile -o nested_subtree -EDESCRIPTION    prune is used to prune a codebook created by tsvq.c.  It uses the    codebook file to find the tree structure, and the statistics file to    find the count and average distortion for each node of the tree.    See pages 683-684 of _Vector Quantization and Signal Compression_    by Gersho & Gray for the pruning algorithm.  The -E option implements    entropy constrained pruning.  The output of the program is a nested    subtree or preorder list (like the codebook) with one long int used for    each node. If the value is zero, it means that nothing was pruned at    that node.  If the value is non-zero, then there was a pruning done at    that node.  The number n stored indicates that that node was the nth    pruning done. Prune prints to stdout for each subtree: the subtree    number, the minimum lambda of that tree, the average rate and distortion,    and the number of nodes in the tree.  Use the program "select" to choose    a given subtree.  Type "prune" to see default values.PROGRAM    select.cSYNOPSIS    select -c codebook -s nested_subtree -n selection -o sub_codebookDESCRIPTION    To get tree number N, just remove all of the nodes that    descend from any node that has  a label of 1,2,...N.    select is used to create a pruned TSVQ codebook from the original    TSVQ codebook.  The input of the program is the codebook file and    the nested subtree file created by prune. The nested subtree file    is a preorder list with one long int used for each node. If the value    is zero, it means that nothing was pruned at that node.  If the value    is non-zero, then a pruning is done at that node.  The number n stored    indicates that the nth pruning pass was done at that node. To get    tree number N, all of the nodes that descend from any    node that has a label of 1,2,...N are pruned.PROGRAM    voronoi_ts.cSYNOPSIS    voronoi_ts -c codebook -o output -r rows -l columnsDESCRIPTION    voronoi_ts takes a TSVQ codebook that has two dimensions and    creates an output image that shows the edges of the voronoi    regions.  The codewords are scaled to fit 90% of the region    defined by the user as rows by columns.