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• Tour of Part of the VP8 Process

  18 novembre 2010, par Multimedia Mike — VP8

  My toy VP8 encoder outputs a lot of textual data to illustrate exactly what it’s doing. For those who may not be exactly clear on how this or related algorithms operate, this may prove illuminating.

  Let’s look at subblock 0 of macroblock 0 of a luma plane :

   subblock 0 (original)
    92  91  89  86
    91  90  88  86
    89  89  89  88
    89  87  88  93
  

  Since it’s in the top-left corner of the image to be encoded, the phantom samples above and to the left are implicitly 128 for the purpose of intra prediction (in the VP8 algorithm).

   subblock 0 (original)
       128 128 128 128
   128  92  91  89  86
   128  91  90  88  86
   128  89  89  89  88
   128  89  87  88  93
  

  
  Using the 4×4 DC prediction mode means averaging the 4 top predictors and 4 left predictors. So, the predictor is 128. Subtract this from each element of the subblock :

   subblock 0, predictor removed
   -36 -37 -39 -42
   -37 -38 -40 -42
   -39 -39 -39 -40
   -39 -41 -40 -35
  

  Next, run the subblock through the forward transform :

   subblock 0, transformed
   -312   7   1   0
      1  12  -5   2
      2  -3   3  -1
      1   0  -2   1
  

  Quantize (integer divide) each element ; the DC (first element) and AC (rest of the elements) quantizers are both 4 :

   subblock 0, quantized
   -78   1   0   0
     0   3  -1   0
     0   0   0   0
     0   0   0   0
  

  The above block contains the coefficients that are actually transmitted (zigzagged and entropy-encoded) through the bitstream and decoded on the other end.

  The decoding process looks something like this– after the same coefficients are decoded and rearranged, they are dequantized (multiplied) by the original quantizers :

   subblock 0, dequantized
   -312   4   0   0
      0  12  -4   0
      0   0   0   0
      0   0   0   0
  

  Note that these coefficients are not exactly the same as the original, pre-quantized coefficients. This is a large part of where the “lossy” in “lossy video compression” comes from.

  Next, the decoder generates a base predictor subblock. In this case, it’s all 128 (DC prediction for top-left subblock) :

   subblock 0, predictor
    128 128 128 128
    128 128 128 128
    128 128 128 128
    128 128 128 128
  

  Finally, the dequantized coefficients are shoved through the inverse transform and added to the base predictor block :

   subblock 0, reconstructed
    91  91  89  85
    90  90  89  87
    89  88  89  90
    88  88  89  92
  

  Again, not exactly the same as the original block, but an incredible facsimile thereof.

  Note that this decoding-after-encoding demonstration is not merely pedagogical– the encoder has to decode the subblock because the encoding of successive subblocks may depend on this subblock. The encoder can’t rely on the original representation of the subblock because the decoder won’t have that– it will have the reconstructed block.

  For example, here’s the next subblock :

   subblock 1 (original)
    84  84  87  90
    85  85  86  93
    86  83  83  89
    91  85  84  87
  

  Let’s assume DC prediction once more. The 4 top predictors are still all 128 since this subblock lies along the top row. However, the 4 left predictors are the right edge of the subblock reconstructed in the previous example :

   subblock 1 (original)
      128 128 128 128
   85  84  84  87  90
   87  85  85  86  93
   90  86  83  83  89
   92  91  85  84  87
  

  The DC predictor is computed as (128 + 128 + 128 + 128 + 85 + 87 + 90 + 92 + 4) / 8 = 108 (the extra +4 is for rounding considerations). (Note that in this case, using the original subblock’s right edge would also have resulted in 108, but that’s beside the point.)

  Continuing through the same process as in subblock 0 :

   subblock 1, predictor removed
   -24 -24 -21 -18
   -23 -23 -22 -15
   -22 -25 -25 -19
   -17 -23 -24 -21
   subblock 1, transformed
  
   -173  -9  14  -1
  
      2 -11  -4   0
  
      1   6  -2   3
  
     -5   1   0   1
   subblock 1, quantized
  
   -43  -2   3   0
  
     0  -2  -1   0
  
     0   1   0   0
  
    -1   0   0   0
   subblock 1, dequantized
  
   -172  -8  12   0
  
      0  -8  -4   0
  
      0   4   0   0
  
     -4   0   0   0
   subblock 1, predictor
  
    108 108 108 108
  
    108 108 108 108
  
    108 108 108 108
  
    108 108 108 108
   subblock 1, reconstructed
  
    84  84  87  89
  
    86  85  87  91
  
    86  83  84  89
  
    90  85  84  88
  

  I hope this concrete example (straight from a working codec) clarifies this part of the VP8 process.

• Revision 7350d13dfa4ed691621b9c42871cca5ca8885093 : ne pas recharger une {{{ {liste a,b,c} }}} a chaque tour git-svn-id : ...

  23 décembre 2010, par Fil — Log

  ne pas recharger une liste a,b,c a chaque tour git-svn-id : svn ://trac.rezo.net/spip/spip@16829 caf5f3e8-d4fe-0310-bb3e-c32d5e47d55d

• How to manage hls in Nginx RTMP module

  27 mars, par syrkandonut

  I would like to manage the hls broadcast on request, like stop/start or some other way in Nginx RMTP module.
My rtmp server needs to support many cameras, however, when it does ffmpeg exec for 200-300 rtmp streams, this is very difficult for the processor, so I would like to execute the ffmpeg command in parallel only on request, how could this be done ?

  


  Rtmp Server

  


  rtmp {
    server {
        listen 1935;
        chunk_size 8192;

        application live {
            live on;
            record off;
            drop_idle_publisher 10s;
            allow publish all;

            on_publish rtmp-router:8082/on_publish;

              exec ffmpeg -i rtmp://localhost:1935/live/$name
              -f lavfi -i anullsrc -c:v copy -c:a aac -shortest -f flv rtmp://localhost:1935/hls/$name_main;
        }


        application hls {
            live on;
            hls on;
            hls_fragment_naming system;
            hls_fragment 2;
            hls_playlist_length 4;
            hls_path /opt/data/hls;
            hls_nested on;

            hls_variant _main BANDWIDTH=878000,RESOLUTION=640x360;
        }
    }
}


  


  I would like to solve this through nginx or python itself, since the server running with threads is written in FastAPI.