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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 891793a540 : Conditionally skip reference frame check For regular inter frames, if the dista

  29 septembre 2014, par Jingning Han

  Changed Paths :
   Modify /vp9/encoder/vp9_rdopt.c

  
  
  Conditionally skip reference frame check

  For regular inter frames, if the distance from GOLDEN_FRAME is larger
  than 2 and if the predicted motion vector of LAST_FRAME gives lower
  sse than that of GOLDEN_FRAME, skip the GOLDE_FRAME mode checking in
  the rate-distortion optimization. It provides about 5% speed-up at
  expense of -0.137% and -0.230% performance down for speed 3. Local
  experiment results :

  pedestrian 1080p 2000 kbps
  66712 b/f, 40.908 dB, 113688 ms ->
  66768 b/f, 40.911 dB, 108752 ms

  blue_sky 1080p 2000 kbps
  51054 b/f, 35.894 dB, 70406 ms ->
  51051 b/f, 35.891 dB, 67236 ms

  old_town_cross 720p 1500 kbps
  14412 b/f, 36.252 dB, 60690 ms ->
  14431 b/f, 36.249 dB, 57346 ms

  Change-Id : Idfcafe7f63da7a4896602fc60bd7093f0f0d82ca

• How to insert a key frame(Iframe) to a h.264 video stream in ffmpeg C++ api ?

  8 décembre 2015, par fcjy

  I have a real time video stream, and want to cut some video clips from it by accurate timestamp(pts).

  When I receiver an avpacket, I decode it, and do something and cache the avpacket. I don’t want to re-encode all avpackets, it cost cpu resource.

  There are many gop structure in H.264 stream, usually we should cut the video begin at the key frame, and end at the key frame. Otherwise the front some frames in the video clip would display error.

  Now I use av_write_frame to make avpacket to video. But sometimes the length of gop is very long, such as it could be 250, 8.3s(30 frame per second). It means the distance between two I-frame could be 250 frames. The video clip is short, I don’t want to add too many unused frames.

  How should I do ? I think i should insert a i-frame at the start position of video clip. Could I change a p-frame to i-frame ?

  Thanks your reading !