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• Basic Video Palette Conversion

  20 août 2011, par Multimedia Mike — General, Python

  How do you take a 24-bit RGB image and convert it to an 8-bit paletted image for the purpose of compression using a codec that requires 8-bit input images ? Seems simple enough and that’s what I’m tackling in this post.

  Ask FFmpeg/Libav To Do It
  Ideally, FFmpeg / Libav should be able to handle this automatically. Indeed, FFmpeg used to be able to, at least at the time I wrote this post about ZMBV and was unhappy with FFmpeg’s default results. Somewhere along the line, FFmpeg and Libav lost the ability to do this. I suspect it got removed during some swscale refactoring.

  Still, there’s no telling if the old system would have computed palettes correctly for QuickTime files.

  Distance Approach
  When I started writing my SMC video encoder, I needed to convert RGB (from PNG files) to PAL8 colorspace. The path of least resistance was to match the pixels in the input image to the default 256-color palette that QuickTime assumes (and is hardcoded into FFmpeg/Libav).

  How to perform the matching ? Find the palette entry that is closest to a given input pixel, where "closest" is the minimum distance as computed by the usual distance formula (square root of the sum of the squares of the diffs of all the components).

  
  
  

  That means for each pixel in an image, check the pixel against 256 palette entries (early termination is possible if an acceptable threshold is met). As you might imagine, this can be a bit time-consuming. I wondered about a faster approach...

  Lookup Table
  I think this is the approach that FFmpeg used to use, but I went and derived it for myself after studying the default QuickTime palette table. There’s a pattern there— all of the RGB entries are comprised of combinations of 6 values — 0x00, 0x33, 0x66, 0x99, 0xCC, and 0xFF. If you mix and match these for red, green, and blue values, you come up with 6 * 6 * 6 = 216 different colors. This happens to be identical to the web-safe color palette.

  The first (0th) entry in the table is (FF, FF, FF), followed by (FF, FF, CC), (FF, FF, 99), and on down to (FF, FF, 00) when the green component gets knocked down and step and the next color is (FF, CC, FF). The first 36 palette entries in the table all have a red component of 0xFF. Thus, if an input RGB pixel has a red color closest to 0xFF, it must map to one of those first 36 entries.

  I created a table which maps indices 0..215 to values from 5..0. Each of the R, G, and B components of an input pixel are used to index into this table and derive 3 indices ri, gi, and bi. Finally, the index into the palette table is given by :

    index = ri * 36 + gi * 6 + bi
  

  For example, the pixel (0xFE, 0xFE, 0x01) would yield ri, gi, and bi values of 0, 0, and 5. Therefore :

    index = 0 * 36 + 0 * 6 + 5
  

  The palette index is 5, which maps to color (0xFF, 0xFF, 0x00).

  Validation
  So I was pretty pleased with myself for coming up with that. Now, ideally, swapping out one algorithm for another in my SMC encoder should yield identical results. That wasn’t the case, initially.

  One problem is that the regulation QuickTime palette actually has 40 more entries above and beyond the typical 216-entry color cube (rounding out the grand total of 256 colors). Thus, using the distance approach with the full default table provides for a little more accuracy.

  However, there still seems to be a problem. Let’s check our old standby, the Big Buck Bunny logo image :

  
  
  Distance approach using the full 256-color QuickTime default palette
  
  
  Distance approach using the 216-color palette
  
  
  Table lookup approach using the 216-color palette
  

  I can’t quite account for that big red splotch there. That’s the most notable difference between images 1 and 2 and the only visible difference between images 2 and 3.

  To prove to myself that the distance approach is equivalent to the table approach, I wrote a Python script to iterate through all possible RGB combinations and verify the equivalence. If you’re not up on your base 2 math, that’s 2²⁴ or 16,777,216 colors to run through. I used Python’s multiprocessing module to great effect and really maximized a Core i7 CPU with 8 hardware threads.

  So I’m confident that the palette conversion techniques are sound. The red spot is probably attributable to a bug in my WIP SMC encoder.

  Source Code
  Update August 23, 2011 : Here’s the Python code I used for proving equivalence between the 2 approaches. In terms of leveraging multiple CPUs, it’s possibly the best program I have written to date.

  PLAIN TEXT

  PYTHON :

  1. # !/usr/bin/python
  2.  
  3. from multiprocessing import Pool
  4.  
  5. palette = []
  6. pal8_table = []
  7.  
  8. def process_r(r) :
  9.  counts = []
  10.  
  11.  for i in xrange(216) :
  12.   counts.append(0)
  13.  
  14.  print "r = %d" % (r)
  15.  for g in xrange(256) :
  16.   for b in xrange(256) :
  17.    min_dsqrd = 0xFFFFFFFF
  18.    best_index = 0
  19.    for i in xrange(len(palette)) :
  20.     dr = palette[i][0] - r
  21.     dg = palette[i][1] - g
  22.     db = palette[i][2] - b
  23.     dsqrd = dr * dr + dg * dg + db * db
  24.     if dsqrd <min_dsqrd :
  25.      min_dsqrd = dsqrd
  26.      best_index = i
  27.    counts[best_index] += 1
  28.  
  29.    # check if the distance approach deviates from the table-based approach
  30.    i = best_index
  31.    r = palette[i][0]
  32.    g = palette[i][1]
  33.    b = palette[i][2]
  34.    ri = pal8_table[r]
  35.    gi = pal8_table[g]
  36.    bi = pal8_table[b]
  37.    table_index = ri * 36 + gi * 6 + bi ;
  38.    if table_index != best_index :
  39.     print "(0x%02X 0x%02X 0x%02X) : distance index = %d, table index = %d" % (r, g, b, best_index, table_index)
  40.  
  41.  return counts
  42.  
  43. if __name__ == ’__main__’ :
  44.  counts = []
  45.  for i in xrange(216) :
  46.   counts.append(0)
  47.  
  48.  # initialize reference palette
  49.  color_steps = [ 0xFF, 0xCC, 0x99, 0x66, 0x33, 0x00 ]
  50.  for r in color_steps :
  51.   for g in color_steps :
  52.    for b in color_steps :
  53.     palette.append([r, g, b])
  54.  
  55.  # initialize palette conversion table
  56.  for i in range(0, 26) :
  57.   pal8_table.append(5)
  58.  for i in range(26, 77) :
  59.   pal8_table.append(4)
  60.  for i in range(77, 128) :
  61.   pal8_table.append(3)
  62.  for i in range(128, 179) :
  63.   pal8_table.append(2)
  64.  for i in range(179, 230) :
  65.   pal8_table.append(1)
  66.  for i in range(230, 256) :
  67.   pal8_table.append(0)
  68.  
  69.  # create a pool of worker threads and break up the overall job
  70.  pool = Pool()
  71.  it = pool.imap_unordered(process_r, range(256))
  72.  try :
  73.   while 1 :
  74.    partial_counts = it.next()
  75.    for i in xrange(216) :
  76.     counts[i] += partial_counts[i]
  77.  except StopIteration :
  78.   pass
  79.  
  80.  print "index, count, red, green, blue"
  81.  for i in xrange(len(counts)) :
  82.   print "%d, %d, %d, %d, %d" % (i, counts[i], palette[i][0], palette[i][1], palette[i][2])

• Revision 82593 : Support de google analytics universal : version 0.4.6 * Le mode universal ...

  19 mai 2014, par cam.lafit@… — Log

  Support de google analytics universal : version 0.4.6
  * Le mode universal est prise en charge, il s’agit d’une case à cocher pour prendre en compte le script à charger
  * La meta est préfixée ga pour éviter un conflit avec le mot potentiellement générique "universal"
  Version testée et validée pour spip 3.0
  Référence : ​https://developers.google.com/analytics/devguides/collection/upgrade/reference/gajs-analyticsjs

• H264 to MP4 with Bframe play back and forth on google chrome[ffmpeg]

  2 mai 2017, par Ravi Agola

  I want to generate a mp4 container with h264 encoded file.

  H264 file contains [I P B B][P B B][P B B][P B B] frames.

  when I generate a mp4 file with FFmpeg, It works well with FFplay as well as VLC but on google-chrome mp4 file plays frame back and forth.

  ffmpeg -i input.h264 -vcodec copy output.mp4

  when i use internal codec library(libx264) it works well on (ffplay,vlc and google-chrome)

  ffmpeg -i input.h264 -vcodec h264 output.mp4

  as above command transcode h264(native) to h264(x264), I don’t want to transcode file as I will be using it with ffmpeg library.

  when I use h264 without B frame it works well in both cases.

  I have tried some experiment with sample test file available here.

  direct conversion(MKV to MP4) works well with chrome as below

  ffmpeg -i jellyfish-3-mbps-hd-h264.mkv -vcodec copy output.mp4

  (MKV to H264 and H264 to MP4) plays back and forth on chrome

  ffmpeg -i jellyfish-3-mbps-hd-h264.mkv -vcodec copy output.h264
  ffmpeg -i output.h264 -vcodec copy output.mp4

  I get message in this case
  [mp4 @ 0xb6f8b20] Timestamps are unset in a packet for stream 0. This is deprecated and will stop working in the future. Fix your code to set the timestamps properly
  [mp4 @ 0xb6f8b20] pts has no value"

  what can be a reason behind this behavior ?

  Thanks.