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Imported from libpng-1.0.1e.tar

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Glenn Randers-Pehrson
1998-06-06 15:31:35 -05:00
parent 8686fff347
commit f7d1a17033
33 changed files with 782 additions and 258 deletions
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21
pngwutil.c
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@@ -1,12 +1,11 @@
/* pngwutil.c - utilities to write a PNG file
*
* 1.0.1d
* libpng 1.0.1e - June 6, 1998
* For conditions of distribution and use, see copyright notice in png.h
* Copyright (c) 1995, 1996 Guy Eric Schalnat, Group 42, Inc.
* Copyright (c) 1996, 1997 Andreas Dilger
* Copyright (c) 1998, Glenn Randers-Pehrson
* May 21, 1998
*/
#define PNG_INTERNAL
@@ -575,7 +574,8 @@ png_write_hIST(png_structp png_ptr, png_uint_16p hist, int num_hist)
}
#endif
#if defined(PNG_WRITE_tEXt_SUPPORTED) || defined(PNG_WRITE_zTXt_SUPPORTED)
#if defined(PNG_WRITE_tEXt_SUPPORTED) || defined(PNG_WRITE_zTXt_SUPPORTED) || \
defined(PNG_WRITE_pCAL_SUPPORTED)
/* Check that the tEXt or zTXt keyword is valid per PNG 1.0 specification,
* and if invalid, correct the keyword rather than discarding the entire
* chunk. The PNG 1.0 specification requires keywords 1-79 characters in
@@ -1416,10 +1416,19 @@ png_write_find_filter(png_structp png_ptr, png_row_infop row_info)
* as the "minimum sum of absolute differences" heuristic. Other
* heuristics are the "weighted minimum sum of absolute differences"
* (experimental and can in theory improve compression), and the "zlib
* predictive" method (not implemented in libpng 0.95), which does test
* compressions of lines using different filter methods, and then chooses
* the (series of) filter(s) which give minimum compressed data size (VERY
* predictive" method (not implemented yet), which does test compressions
* of lines using different filter methods, and then chooses the
* (series of) filter(s) that give minimum compressed data size (VERY
* computationally expensive).
*
* GRR 980525: consider also
* (1) minimum sum of absolute differences from running average (i.e.,
* keep running sum of non-absolute differences & count of bytes)
* [track dispersion, too? restart average if dispersion too large?]
* (1b) minimum sum of absolute differences from sliding average, probably
* with window size <= deflate window (usually 32K)
* (2) minimum sum of squared differences from zero or running average
* (i.e., ~ root-mean-square approach)
*/