jquant2.pas

用pascal寫的jpeg codec, 測試過的

Unit JQuant2;


{ This file contains 2-pass color quantization (color mapping) routines.
  These routines provide selection of a custom color map for an image,
  followed by mapping of the image to that color map, with optional
  Floyd-Steinberg dithering.
  It is also possible to use just the second pass to map to an arbitrary
  externally-given color map.

  Note: ordered dithering is not supported, since there isn't any fast
  way to compute intercolor distances; it's unclear that ordered dither's
  fundamental assumptions even hold with an irregularly spaced color map. }

{ Original: jquant2.c; Copyright (C) 1991-1996, Thomas G. Lane. }

interface

{$I jconfig.inc}

uses
  jmorecfg,
  jdeferr,
  jerror,
  jutils,
  jpeglib;

{ Module initialization routine for 2-pass color quantization. }


{GLOBAL}
procedure jinit_2pass_quantizer (cinfo : j_decompress_ptr);

implementation

{ This module implements the well-known Heckbert paradigm for color
  quantization.  Most of the ideas used here can be traced back to
  Heckbert's seminal paper
    Heckbert, Paul.  "Color Image Quantization for Frame Buffer Display",
    Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304.

  In the first pass over the image, we accumulate a histogram showing the
  usage count of each possible color.  To keep the histogram to a reasonable
  size, we reduce the precision of the input; typical practice is to retain
  5 or 6 bits per color, so that 8 or 4 different input values are counted
  in the same histogram cell.

  Next, the color-selection step begins with a box representing the whole
  color space, and repeatedly splits the "largest" remaining box until we
  have as many boxes as desired colors.  Then the mean color in each
  remaining box becomes one of the possible output colors.

  The second pass over the image maps each input pixel to the closest output
  color (optionally after applying a Floyd-Steinberg dithering correction).
  This mapping is logically trivial, but making it go fast enough requires
  considerable care.

  Heckbert-style quantizers vary a good deal in their policies for choosing
  the "largest" box and deciding where to cut it.  The particular policies
  used here have proved out well in experimental comparisons, but better ones
  may yet be found.

  In earlier versions of the IJG code, this module quantized in YCbCr color
  space, processing the raw upsampled data without a color conversion step.
  This allowed the color conversion math to be done only once per colormap
  entry, not once per pixel.  However, that optimization precluded other
  useful optimizations (such as merging color conversion with upsampling)
  and it also interfered with desired capabilities such as quantizing to an
  externally-supplied colormap.  We have therefore abandoned that approach.
  The present code works in the post-conversion color space, typically RGB.

  To improve the visual quality of the results, we actually work in scaled
  RGB space, giving G distances more weight than R, and R in turn more than
  B.  To do everything in integer math, we must use integer scale factors.
  The 2/3/1 scale factors used here correspond loosely to the relative
  weights of the colors in the NTSC grayscale equation.
  If you want to use this code to quantize a non-RGB color space, you'll
  probably need to change these scale factors. }

const
  R_SCALE = 2;          { scale R distances by this much }
  G_SCALE = 3;          { scale G distances by this much }
  B_SCALE = 1;          { and B by this much }

{ Relabel R/G/B as components 0/1/2, respecting the RGB ordering defined
  in jmorecfg.h.  As the code stands, it will do the right thing for R,G,B
  and B,G,R orders.  If you define some other weird order in jmorecfg.h,
  you'll get compile errors until you extend this logic.  In that case
  you'll probably want to tweak the histogram sizes too. }

{$ifdef RGB_RED_IS_0}
const
  C0_SCALE = R_SCALE;
  C1_SCALE = G_SCALE;
  C2_SCALE = B_SCALE;
{$else}
const
  C0_SCALE = B_SCALE;
  C1_SCALE = G_SCALE;
  C2_SCALE = R_SCALE;
{$endif}


{ First we have the histogram data structure and routines for creating it.

  The number of bits of precision can be adjusted by changing these symbols.
  We recommend keeping 6 bits for G and 5 each for R and B.
  If you have plenty of memory and cycles, 6 bits all around gives marginally
  better results; if you are short of memory, 5 bits all around will save
  some space but degrade the results.
  To maintain a fully accurate histogram, we'd need to allocate a "long"
  (preferably unsigned long) for each cell.  In practice this is overkill;
  we can get by with 16 bits per cell.  Few of the cell counts will overflow,
  and clamping those that do overflow to the maximum value will give close-
  enough results.  This reduces the recommended histogram size from 256Kb
  to 128Kb, which is a useful savings on PC-class machines.
  (In the second pass the histogram space is re-used for pixel mapping data;
  in that capacity, each cell must be able to store zero to the number of
  desired colors.  16 bits/cell is plenty for that too.)
  Since the JPEG code is intended to run in small memory model on 80x86
  machines, we can't just allocate the histogram in one chunk.  Instead
  of a true 3-D array, we use a row of pointers to 2-D arrays.  Each
  pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and
  each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries.  Note that
  on 80x86 machines, the pointer row is in near memory but the actual
  arrays are in far memory (same arrangement as we use for image arrays). }


const
  MAXNUMCOLORS = (MAXJSAMPLE+1);        { maximum size of colormap }

{ These will do the right thing for either R,G,B or B,G,R color order,
  but you may not like the results for other color orders. }

const
  HIST_C0_BITS = 5;             { bits of precision in R/B histogram }
  HIST_C1_BITS = 6;             { bits of precision in G histogram }
  HIST_C2_BITS = 5;             { bits of precision in B/R histogram }

{ Number of elements along histogram axes. }
const
  HIST_C0_ELEMS = (1 shl HIST_C0_BITS);
  HIST_C1_ELEMS = (1 shl HIST_C1_BITS);
  HIST_C2_ELEMS = (1 shl HIST_C2_BITS);

{ These are the amounts to shift an input value to get a histogram index. }
const
  C0_SHIFT = (BITS_IN_JSAMPLE-HIST_C0_BITS);
  C1_SHIFT = (BITS_IN_JSAMPLE-HIST_C1_BITS);
  C2_SHIFT = (BITS_IN_JSAMPLE-HIST_C2_BITS);


type                            { Nomssi }
  RGBptr = ^RGBtype;
  RGBtype = packed record
    r,g,b : JSAMPLE;
  end;
type
  histcell = UINT16;            { histogram cell; prefer an unsigned type }

type
  histptr = ^histcell {FAR};       { for pointers to histogram cells }

type
  hist1d = array[0..HIST_C2_ELEMS-1] of histcell; { typedefs for the array }
  {hist1d_ptr = ^hist1d;}
  hist1d_field = array[0..HIST_C1_ELEMS-1] of hist1d;
                                  { type for the 2nd-level pointers }
  hist2d = ^hist1d_field;
  hist2d_field = array[0..HIST_C0_ELEMS-1] of hist2d;
  hist3d = ^hist2d_field;	  { type for top-level pointer }


{ Declarations for Floyd-Steinberg dithering.

  Errors are accumulated into the array fserrors[], at a resolution of
  1/16th of a pixel count.  The error at a given pixel is propagated
  to its not-yet-processed neighbors using the standard F-S fractions,
 		...	(here)	7/16
 		3/16	5/16	1/16
  We work left-to-right on even rows, right-to-left on odd rows.

  We can get away with a single array (holding one row's worth of errors)
  by using it to store the current row's errors at pixel columns not yet
  processed, but the next row's errors at columns already processed.  We
  need only a few extra variables to hold the errors immediately around the
  current column.  (If we are lucky, those variables are in registers, but
  even if not, they're probably cheaper to access than array elements are.)

  The fserrors[] array has (#columns + 2) entries; the extra entry at
  each end saves us from special-casing the first and last pixels.
  Each entry is three values long, one value for each color component.

  Note: on a wide image, we might not have enough room in a PC's near data
  segment to hold the error array; so it is allocated with alloc_large. }


{$ifdef BITS_IN_JSAMPLE_IS_8}
type
  FSERROR = INT16;              { 16 bits should be enough }
  LOCFSERROR = int;             { use 'int' for calculation temps }
{$else}
type
  FSERROR = INT32;              { may need more than 16 bits }
  LOCFSERROR = INT32;           { be sure calculation temps are big enough }
{$endif}
type                            { Nomssi }
  RGB_FSERROR_PTR = ^RGB_FSERROR;
  RGB_FSERROR = packed record
    r,g,b : FSERROR;
  end;
  LOCRGB_FSERROR = packed record
    r,g,b : LOCFSERROR;
  end;

type
  FSERROR_PTR = ^FSERROR;
  jFSError = 0..(MaxInt div SIZEOF(RGB_FSERROR))-1;
  FS_ERROR_FIELD = array[jFSError] of RGB_FSERROR;
  FS_ERROR_FIELD_PTR = ^FS_ERROR_FIELD;{far}
                                { pointer to error array (in FAR storage!) }

type
  error_limit_array = array[-MAXJSAMPLE..MAXJSAMPLE] of int;
  { table for clamping the applied error }
  error_limit_ptr = ^error_limit_array;

{ Private subobject }
type
  my_cquantize_ptr = ^my_cquantizer;
  my_cquantizer = record
    pub : jpeg_color_quantizer; { public fields }

    { Space for the eventually created colormap is stashed here }
    sv_colormap : JSAMPARRAY;	{ colormap allocated at init time }
    desired : int;              { desired # of colors = size of colormap }

    { Variables for accumulating image statistics }
    histogram : hist3d;         { pointer to the histogram }

    needs_zeroed : boolean;     { TRUE if next pass must zero histogram }

    { Variables for Floyd-Steinberg dithering }
    fserrors : FS_ERROR_FIELD_PTR;        { accumulated errors }
    on_odd_row : boolean;       { flag to remember which row we are on }
    error_limiter : error_limit_ptr; { table for clamping the applied error }
  end;



{ Prescan some rows of pixels.
  In this module the prescan simply updates the histogram, which has been
  initialized to zeroes by start_pass.
  An output_buf parameter is required by the method signature, but no data
  is actually output (in fact the buffer controller is probably passing a
  NIL pointer). }

{METHODDEF}
procedure prescan_quantize (cinfo : j_decompress_ptr;
                            input_buf : JSAMPARRAY;
                            output_buf : JSAMPARRAY;
                            num_rows : int); far;
var
  cquantize : my_cquantize_ptr;
  {register} ptr : RGBptr;
  {register} histp : histptr;
  {register} histogram : hist3d;
  row : int;
  col : JDIMENSION;
  width : JDIMENSION;
begin
  cquantize := my_cquantize_ptr(cinfo^.cquantize);
  histogram := cquantize^.histogram;
  width := cinfo^.output_width;

  for row := 0 to pred(num_rows) do
  begin
    ptr := RGBptr(input_buf^[row]);
    for col := pred(width) downto 0 do
    begin
      { get pixel value and index into the histogram }
      histp := @(histogram^[GETJSAMPLE(ptr^.r) shr C0_SHIFT]^
                           [GETJSAMPLE(ptr^.g) shr C1_SHIFT]
			   [GETJSAMPLE(ptr^.b) shr C2_SHIFT]);
      { increment, check for overflow and undo increment if so. }
      Inc(histp^);
      if (histp^ <= 0) then
	Dec(histp^);
      Inc(ptr);
    end;
  end;
end;

{ Next we have the really interesting routines: selection of a colormap
  given the completed histogram.
  These routines work with a list of "boxes", each representing a rectangular
  subset of the input color space (to histogram precision). }

type
  box = record
  { The bounds of the box (inclusive); expressed as histogram indexes }
    c0min, c0max : int;
    c1min, c1max : int;
    c2min, c2max : int;
    { The volume (actually 2-norm) of the box }
    volume : INT32;
    { The number of nonzero histogram cells within this box }
    colorcount : long;
  end;

type
  jBoxList = 0..(MaxInt div SizeOf(box))-1;
  box_field = array[jBoxlist] of box;
  boxlistptr = ^box_field;
  boxptr = ^box;

{LOCAL}
function find_biggest_color_pop (boxlist : boxlistptr; numboxes : int) : boxptr;
{ Find the splittable box with the largest color population }
{ Returns NIL if no splittable boxes remain }
var
  boxp : boxptr ; {register}
  i : int;        {register}
  maxc : long;    {register}
  which : boxptr;
begin
  which := NIL;
  boxp := @(boxlist^[0]);
  maxc := 0;
  for i := 0 to pred(numboxes) do
  begin
    if (boxp^.colorcount > maxc) and (boxp^.volume > 0) then
    begin
      which := boxp;
      maxc := boxp^.colorcount;
    end;
    Inc(boxp);
  end;
  find_biggest_color_pop := which;
end;


{LOCAL}
function find_biggest_volume (boxlist : boxlistptr; numboxes : int) : boxptr;
{ Find the splittable box with the largest (scaled) volume }
{ Returns NULL if no splittable boxes remain }
var
  {register} boxp : boxptr;
  {register} i : int;
  {register} maxv : INT32;
  which : boxptr;
begin
  maxv := 0;
  which := NIL;
  boxp := @(boxlist^[0]);
  for i := 0 to pred(numboxes) do
  begin
    if (boxp^.volume > maxv) then
    begin
      which := boxp;
      maxv := boxp^.volume;
    end;
    Inc(boxp);
  end;
  find_biggest_volume := which;
end;


{LOCAL}
procedure update_box (cinfo : j_decompress_ptr; var boxp : box);
label
  have_c0min, have_c0max,
  have_c1min, have_c1max,
  have_c2min, have_c2max;
{ Shrink the min/max bounds of a box to enclose only nonzero elements, }
{ and recompute its volume and population }
var
  cquantize : my_cquantize_ptr;
  histogram : hist3d;
  histp : histptr;
  c0,c1,c2 : int;
  c0min,c0max,c1min,c1max,c2min,c2max : int;
  dist0,dist1,dist2 : INT32;
  ccount : long;
begin
  cquantize := my_cquantize_ptr(cinfo^.cquantize);
  histogram := cquantize^.histogram;