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• Muxing Android MediaCodec encoded H264 packets into RTMP

  31 décembre 2015, par Vadym

  I am coming from a thread Encoding H.264 from camera with Android MediaCodec. My setup is very similar. However, I attempt to write mux the encoded frames and with javacv and broadcast them via rtmp.

  RtmpClient.java

  ...
  
  private volatile BlockingQueue mFrameQueue = new LinkedBlockingQueue(MAXIMUM_VIDEO_FRAME_BACKLOG);
  
  ...
  
  private void startStream() throws FrameRecorder.Exception, IOException {
  
      if (TextUtils.isEmpty(mDestination)) {
  
          throw new IllegalStateException("Cannot start RtmpClient without destination");
  
      }
  
  
  
      if (mCamera == null) {
  
          throw new IllegalStateException("Cannot start RtmpClient without camera.");
  
      }
  
  
  
      Camera.Parameters cameraParams = mCamera.getParameters();
  
  
  
      mRecorder = new FFmpegFrameRecorder(
  
              mDestination,
  
              mVideoQuality.resX,
  
              mVideoQuality.resY,
  
              (mAudioQuality.channelType.equals(AudioQuality.CHANNEL_TYPE_STEREO) ? 2 : 1));
  
  
  
      mRecorder.setFormat("flv");
  
  
  
      mRecorder.setFrameRate(mVideoQuality.frameRate);
  
      mRecorder.setVideoBitrate(mVideoQuality.bitRate);
  
      mRecorder.setVideoCodec(avcodec.AV_CODEC_ID_H264);
  
  
  
      mRecorder.setSampleRate(mAudioQuality.samplingRate);
  
      mRecorder.setAudioBitrate(mAudioQuality.bitRate);
  
      mRecorder.setAudioCodec(avcodec.AV_CODEC_ID_AAC);
  
  
  
      mVideoStream = new VideoStream(mRecorder, mVideoQuality, mFrameQueue, mCamera);
  
      mAudioStream = new AudioStream(mRecorder, mAudioQuality);
  
  
  
      mRecorder.start();
  
  
  
      // Setup a bufferred preview callback
  
      setupCameraCallback(mCamera, mRtmpClient, DEFAULT_PREVIEW_CALLBACK_BUFFERS,
  
              mVideoQuality.resX * mVideoQuality.resY * ImageFormat.getBitsPerPixel(
  
                      cameraParams.getPreviewFormat())/8);
  
  
  
      try {
  
          mVideoStream.start();
  
          mAudioStream.start();
  
      }
  
      catch(Exception e) {
  
          e.printStackTrace();
  
          stopStream();
  
      }
  
  }
  
  ...
  
  @Override
  
  public void onPreviewFrame(byte[] data, Camera camera) {
  
      boolean frameQueued = false;
  
  
  
      if (mRecorder == null || data == null) {
  
          return;
  
      }
  
  
  
      frameQueued = mFrameQueue.offer(data);
  
  
  
      // return the buffer to be reused - done in videostream
  
      //camera.addCallbackBuffer(data);
  
  }
  
  ...

  VideoStream.java

  ...
  
  @Override
  
  public void run() {
  
      try {
  
          mMediaCodec = MediaCodec.createEncoderByType("video/avc");
  
          MediaFormat mediaFormat = MediaFormat.createVideoFormat("video/avc", mVideoQuality.resX, mVideoQuality.resY);
  
          mediaFormat.setInteger(MediaFormat.KEY_BIT_RATE, mVideoQuality.bitRate);
  
          mediaFormat.setInteger(MediaFormat.KEY_FRAME_RATE, mVideoQuality.frameRate);
  
          mediaFormat.setInteger(MediaFormat.KEY_COLOR_FORMAT, MediaCodecInfo.CodecCapabilities.COLOR_FormatYUV420SemiPlanar);
  
          mediaFormat.setInteger(MediaFormat.KEY_I_FRAME_INTERVAL, 1);
  
          mMediaCodec.configure(mediaFormat, null, null, MediaCodec.CONFIGURE_FLAG_ENCODE);
  
          mMediaCodec.start();
  
      }
  
      catch(IOException e) {
  
          e.printStackTrace();
  
      }
  
  
  
      long startTimestamp = System.currentTimeMillis();
  
      long frameTimestamp = 0;
  
      byte[] rawFrame = null;
  
  
  
      try {
  
          while (!Thread.interrupted()) {
  
              rawFrame = mFrameQueue.take();
  
  
  
              frameTimestamp = 1000 * (System.currentTimeMillis() - startTimestamp);
  
  
  
              encodeFrame(rawFrame, frameTimestamp);
  
  
  
              // return the buffer to be reused
  
              mCamera.addCallbackBuffer(rawFrame);
  
          }
  
      }
  
      catch (InterruptedException ignore) {
  
          // ignore interrup while waiting
  
      }
  
  
  
      // Clean up video stream allocations
  
      try {
  
          mMediaCodec.stop();
  
          mMediaCodec.release();
  
          mOutputStream.flush();
  
          mOutputStream.close();
  
      } catch (Exception e){
  
          e.printStackTrace();
  
      }
  
  }
  
  ...
  
  private void encodeFrame(byte[] input, long timestamp) {
  
      try {
  
          ByteBuffer[] inputBuffers = mMediaCodec.getInputBuffers();
  
          ByteBuffer[] outputBuffers = mMediaCodec.getOutputBuffers();
  
  
  
          int inputBufferIndex = mMediaCodec.dequeueInputBuffer(0);
  
  
  
          if (inputBufferIndex >= 0) {
  
              ByteBuffer inputBuffer = inputBuffers[inputBufferIndex];
  
              inputBuffer.clear();
  
              inputBuffer.put(input);
  
              mMediaCodec.queueInputBuffer(inputBufferIndex, 0, input.length, timestamp, 0);
  
          }
  
  
  
          MediaCodec.BufferInfo bufferInfo = new MediaCodec.BufferInfo();
  
  
  
          int outputBufferIndex = mMediaCodec.dequeueOutputBuffer(bufferInfo, 0);
  
  
  
          if (outputBufferIndex >= 0) {
  
              while (outputBufferIndex >= 0) {
  
                  ByteBuffer outputBuffer = outputBuffers[outputBufferIndex];
  
  
  
                  // Should this be a direct byte buffer?
  
                  byte[] outData = new byte[bufferInfo.size - bufferInfo.offset];
  
                  outputBuffer.get(outData);
  
  
  
                  mFrameRecorder.record(outData, bufferInfo.offset, outData.length, timestamp);
  
  
  
                  mMediaCodec.releaseOutputBuffer(outputBufferIndex, false);
  
                  outputBufferIndex = mMediaCodec.dequeueOutputBuffer(bufferInfo, 0);
  
              }
  
          }
  
          else if (outputBufferIndex == MediaCodec.INFO_OUTPUT_BUFFERS_CHANGED) {
  
              outputBuffers = mMediaCodec.getOutputBuffers();
  
          } else if (outputBufferIndex == MediaCodec.INFO_OUTPUT_FORMAT_CHANGED) {
  
              // ignore for now
  
          }
  
      } catch (Throwable t) {
  
          t.printStackTrace();
  
      }
  
  
  
  }
  
  ...

  FFmpegFrameRecorder.java

  ...
  
  // Hackish codec copy frame recording function
  
  public boolean record(byte[] encodedData, int offset, int length, long frameCount) throws Exception {
  
      int ret;
  
  
  
      if (encodedData == null) {
  
          return false;
  
      }
  
  
  
      av_init_packet(video_pkt);
  
  
  
      // this is why i wondered whether I should get outputbuffer data into direct byte buffer
  
      video_outbuf.put(encodedData, 0, encodedData.length);
  
  
  
      video_pkt.data(video_outbuf);
  
      video_pkt.size(video_outbuf_size);
  
  
  
      video_pkt.pts(frameCount);
  
      video_pkt.dts(frameCount);
  
  
  
      video_pkt.stream_index(video_st.index());
  
  
  
      synchronized (oc) {
  
          /* write the compressed frame in the media file */
  
          if (interleaved && audio_st != null) {
  
              if ((ret = av_interleaved_write_frame(oc, video_pkt)) < 0) {
  
                  throw new Exception("av_interleaved_write_frame() error " + ret + " while writing interleaved video frame.");
  
              }
  
          } else {
  
              if ((ret = av_write_frame(oc, video_pkt)) < 0) {
  
                  throw new Exception("av_write_frame() error " + ret + " while writing video frame.");
  
              }
  
          }
  
      }
  
      return (video_pkt.flags() & AV_PKT_FLAG_KEY) == 1;
  
  }
  
  ...

  When I try to stream the video and run ffprobe on it, I get the following output :

  ffprobe version 2.5.3 Copyright (c) 2007-2015 the FFmpeg developers
  
    built on Jan 19 2015 12:56:57 with gcc 4.1.2 (GCC) 20080704 (Red Hat 4.1.2-55)
  
    configuration: --prefix=/usr --bindir=/usr/bin --datadir=/usr/share/ffmpeg --incdir=/usr/include/ffmpeg --libdir=/usr/lib64 --mandir=/usr/share/man --arch=x86_64 --optflags='-O2 -g -pipe -Wall -Wp,-D_FORTIFY_SOURCE=2 -fexceptions -fstack-protector --param=ssp-buffer-size=4 -m64 -mtune=generic' --enable-bzlib --disable-crystalhd --enable-libass --enable-libdc1394 --enable-libfaac --enable-nonfree --disable-indev=jack --enable-libfreetype --enable-libgsm --enable-libmp3lame --enable-openal --enable-libopencv --enable-libopenjpeg --enable-libopus --enable-librtmp --enable-libtheora --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libxvid --enable-x11grab --enable-avfilter --enable-avresample --enable-postproc --enable-pthreads --disable-static --enable-shared --enable-gpl --disable-debug --disable-stripping --enable-libcaca --shlibdir=/usr/lib64 --enable-runtime-cpudetect
  
    libavutil      54. 15.100 / 54. 15.100
  
    libavcodec     56. 13.100 / 56. 13.100
  
    libavformat    56. 15.102 / 56. 15.102
  
    libavdevice    56.  3.100 / 56.  3.100
  
    libavfilter     5.  2.103 /  5.  2.103
  
    libavresample   2.  1.  0 /  2.  1.  0
  
    libswscale      3.  1.101 /  3.  1.101
  
    libswresample   1.  1.100 /  1.  1.100
  
    libpostproc    53.  3.100 / 53.  3.100
  
  Metadata:
  
    Server                NGINX RTMP (github.com/arut/nginx-rtmp-module)
  
    width                 320.00
  
    height                240.00
  
    displayWidth          320.00
  
    displayHeight         240.00
  
    duration              0.00
  
    framerate             0.00
  
    fps                   0.00
  
    videodatarate         261.00
  
    videocodecid          7.00
  
    audiodatarate         62.00
  
    audiocodecid          10.00
  
    profile
  
    level
  
  [live_flv @ 0x1edb0820] Could not find codec parameters for stream 0 (Video: none, none, 267 kb/s): unknown codec
  
  Consider increasing the value for the 'analyzeduration' and 'probesize' options
  
  Input #0, live_flv, from 'rtmp://<server>/input/<stream>':
  
    Metadata:
  
      Server          : NGINX RTMP (github.com/arut/nginx-rtmp-module)
  
      displayWidth    : 320
  
      displayHeight   : 240
  
      fps             : 0
  
      profile         :
  
      level           :
  
    Duration: 00:00:00.00, start: 16.768000, bitrate: N/A
  
      Stream #0:0: Video: none, none, 267 kb/s, 1k tbr, 1k tbn, 1k tbc
  
      Stream #0:1: Audio: aac (LC), 16000 Hz, mono, fltp, 63 kb/s
  
  Unsupported codec with id 0 for input stream 0
  
  </stream></server>

  I am not, by any means, an expert in H264 or video encoding. I know that the encoded frames that come out from MediaCodec contain SPS NAL, PPS NAL, and frame NAL units. I’ve also written the MediaCodec output into a file and was able to play it back (I did have to specify the format and framerate as otherwise it would play too fast).

  My assumption is that things should work (see how little I know :)). Knowing that SPS and PPS are written out, decoder should know enough. Yet, ffprobe fails to recognize codec, fps, and other video information. Do I need to pass packet flag information to FFmpegFrameRecorder.java:record() function ? Or should I use direct buffer ? Any suggestion will be appreciated ! I should figure things out with a hint.

  PS : I know that some codecs use Planar and other SemiPlanar color formats. That distinction will come later if I get past this. Also, I didn’t go the Surface to MediaCodec way because I need to support API 17 and it requires more changes than this route, which I think helps me understand the more basic flow. Agan, I appreciate any suggestions. Please let me know if something needs to be clarified.

  Update #1

  So having done more testing, I see that my encoder outputs the following frames :

  000000016742800DDA0507E806D0A1350000000168CE06E2
  
  0000000165B840A6F1E7F0EA24000AE73BEB5F51CC7000233A84240...
  
  0000000141E2031364E387FD4F9BB3D67F51CC7000279B9F9CFE811...
  
  0000000141E40304423FFFFF0B7867F89FAFFFFFFFFFFCBE8EF25E6...
  
  0000000141E602899A3512EF8AEAD1379F0650CC3F905131504F839...
  
  ...

  The very first frame contains SPS and PPS. From what I was able to see, these are transmitted only once. The rest are NAL types 1 and 5. So, my assumption is that, for ffprobe to see stream info not only when the stream starts, I should capture SPS and PPS frames and re-transmit them myself periodically, after a certain number of frames, or perhaps before every I-frame. What do you think ?

  Update #2

  Unable to validate that I’m writing frames successfully. After having tried to read back the written packet, I cannot validate written bytes. As strange, on successful write of IPL image and streaming, I also cannot print out bytes of encoded packet after avcodec_encode_video2. Hit the official dead end.

• ffmpeg encode video produce incorrect mediainfo encoding settings

  25 août 2021, par Foong

  I've been trying to do batch encode videos to H265 format. I am using media-autobuild_suite to build ffmpeg and have already updating ffmpeg to the latest version.

  


  ffmpeg version N-103367-g5ddb4b6a1b-g88b3e31562+1 Copyright (c) 2000-2021 the FFmpeg developers
built with gcc 10.3.0 (Rev5, Built by MSYS2 project)
configuration:  --pkg-config=pkgconf --cc='ccache gcc' --cxx='ccache g++' --ld='ccache g++' --disable-autodetect --enable-amf --enable-bzlib --enable-cuda --enable-cuvid --enable-d3d11va --enable-dxva2 --enable-iconv --enable-lzma --enable-nvenc --enable-schannel --enable-zlib --enable-sdl2 --enable-ffnvcodec --enable-nvdec --enable-cuda-llvm --enable-gmp --enable-libmp3lame --enable-libopus --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 --enable-libdav1d --enable-libaom --disable-debug --enable-libfdk-aac --extra-libs=-liconv --enable-gpl --enable-version3 --enable-nonfree
libavutil      57.  4.101 / 57.  4.101
libavcodec     59.  5.101 / 59.  5.101
libavformat    59.  4.102 / 59.  4.102
libavdevice    59.  0.101 / 59.  0.101
libavfilter     8.  3.100 /  8.  3.100
libswscale      6.  0.100 /  6.  0.100
libswresample   4.  0.100 /  4.  0.100
libpostproc    56.  0.100 / 56.  0.100


  


  However, the Writing library and Encoding settings output is incorrect. Before update ffmpeg doesn't have this issue. I wonder what have been missing that causing this issue.

  


  encoding CLI :

  


  ffmpeg -y -hide_banner -loglevel error -stats -hwaccel dxva2 -i "input.mkv" -c:v libx265 -vsync cfr -pix_fmt yuv420p10le -preset fast -tune animation -x265-params ctu=32:min-cu-size=8:max-tu-size=16:tu-intra-depth=2:tu-inter-depth=2:me=1:subme=3:merange=44:max-merge=2:keyint=250:min-keyint=23:rc-lookahead=60:lookahead-slices=6:bframes=6:bframe-bias=0:b-adapt=2:ref=6:limit-refs=3:limit-tu=1:aq-mode=3:aq-strength=0.6:rd=3:psy-rd=1.00:psy-rdoq=1.50:rdoq-level=1:deblock=-1,-1:crf=21.0:qblur=0.50:qcomp=0.60:qpmin=0:qpmax=51:frame-threads=1:strong-intra-smoothing=1:no-lossless=1:no-cu-lossless=1:constrained-intra=1:no-fast-intra=1:no-open-gop=1:no-temporal-layers=1:no-limit-modes=1:weightp=1:no-weightb=1:no-analyze-src-pics=1:no-rd-refine=1:signhide=1:sao=1:no-sao-non-deblock=1:b-pyramid=1:no-cutree=1:no-intra-refresh=1:no-amp=1:temporal-mvp=1:no-early-skip=1:no-tskip=1:no-tskip-fast=1:no-deblock=1:no-b-intra=1:no-splitrd-skip=1:no-strict-cbr=1:no-rc-grain=1:no-const-vbv=1:no-opt-qp-pps=1:no-opt-ref-list-length-pps=1:no-multi-pass-opt-rps=1:no-opt-cu-delta-qp=1:no-hdr=1:no-hdr-opt=1:no-dhdr10-opt=1:no-idr-recovery-sei=1:no-limit-sao=1:no-lowpass-dct=1:no-dynamic-refine=1:no-single-sei=1 -c:a libfdk_aac -vf "fps=fps=29.970,setdar=16/9,scale=960:540:flags=lanczos" -map 0:v:? -map 0:a:? -map_metadata:g -1 -map_chapters 0 "output.mkv"


  


  Input video info :

  


  Video
ID                          : 1
Format                      : AVC
Format/Info                 : Advanced Video Codec
Format profile              : High@L3
Format settings             : CABAC / 5 Ref Frames
Format settings, CABAC      : Yes
Format settings, Reference  : 5 frames
Codec ID                    : V_MPEG4/ISO/AVC
Bit rate                    : 1 595 kb/s
Nominal bit rate            : 2 030 kb/s
Width                       : 720 pixels
Height                      : 480 pixels
Display aspect ratio        : 16:9
Original display aspect rat : 3:2
Frame rate mode             : Variable
Original frame rate         : 29.970 FPS
Color space                 : YUV
Chroma subsampling          : 4:2:0
Bit depth                   : 8 bits
Scan type                   : Progressive
Bits/(Pixel*Frame)          : 0.196
Writing library             : x264 core 66 r1115M 11863ac
Encoding settings           : cabac=1 / ref=5 / deblock=1:1:1 / analyse=0x3:0x133 / me=esa / subme=7 / psy_rd=1.0:0.0 / mixed_ref=1 / me_range=16 / chroma_me=1 / trellis=1 / 8x8dct=1 / cqm=2 / deadzone=21,11 / chroma_qp_offset=-2 / threads=1 / nr=0 / decimate=0 / mbaff=0 / bframes=1 / b_pyramid=0 / b_adapt=1 / b_bias=0 / direct=3 / wpredb=1 / keyint=250 / keyint_min=25 / scenecut=40 / rc=2pass / bitrate=2030 / ratetol=1.0 / qcomp=0.60 / qpmin=10 / qpmax=51 / qpstep=4 / cplxblur=20.0 / qblur=0.5 / ip_ratio=1.40 / pb_ratio=1.30 / aq=1:1.00
Default                     : Yes
Forced                      : No


  


  Output video info :

  


  Video
ID                          : 1
Format                      : HEVC
Format/Info                 : High Efficiency Video Coding
Format profile              : Main 10@L3.1@Main
Codec ID                    : V_MPEGH/ISO/HEVC
Duration                    : 23 min 29 s
Width                       : 960 pixels
Height                      : 540 pixels
Display aspect ratio        : 16:9
Frame rate mode             : Constant
Frame rate                  : 29.970 (29970/1000) FPS
Color space                 : YUV
Chroma subsampling          : 4:2:0
Bit depth                   : 10 bits
Writing library             : Lavc59.5.100 libx265
Encoding settings           : cabac=1 / ref=5 / deblock=1:1:1 / analyse=0x3:0x133 / me=esa / subme=7 / psy_rd=1.0:0.0 / mixed_ref=1 / me_range=16 / chroma_me=1 / trellis=1 / cqm=2 / deadzone=21,11 / chroma_qp_offset=-2 / threads=1 / nr=0 / decimate=0 / mbaff=0 / bframes=1 / b_pyramid=0 / b_adapt=1 / b_bias=0 / direct=3 / wpredb=1 / keyint=250 / keyint_min=25 / scenecut=40 / rc=2pass / bitrate=2030 / ratetol=1.0 / qcomp=0.60 / qpmin=10 / qpmax=51 / qpstep=4 / cplxblur=20.0 / qblur=0.5 / ip_ratio=1.40 / pb_ratio=1.30 / aq=1:1.00
Default                     : Yes
Forced                      : No
Color range                 : Limited


  


  Expecting (from previous encoded videos) :

  


  Video
ID                          : 1
Format                      : HEVC
Format/Info                 : High Efficiency Video Coding
Format profile              : Main 10@L3.1@Main
Codec ID                    : V_MPEGH/ISO/HEVC
Duration                    : 24 min 37 s
Bit rate                    : 833 kb/s
Width                       : 960 pixels
Height                      : 720 pixels
Display aspect ratio        : 4:3
Frame rate mode             : Constant
Frame rate                  : 23.976 (23976/1000) FPS
Color space                 : YUV
Chroma subsampling          : 4:2:0
Bit depth                   : 10 bits
Bits/(Pixel*Frame)          : 0.050
Stream size                 : 147 MiB
Title                       : HEVC
Writing library             : x265 3.4+28-419182243:[Windows][GCC 10.2.0][64 bit] 10bit
Encoding settings           : cpuid=1111039 / frame-threads=3 / numa-pools=12 / wpp / no-pmode / no-pme / no-psnr / no-ssim / log-level=2 / input-csp=1 / input-res=960x720 / interlace=0 / total-frames=0 / level-idc=0 / high-tier=1 / uhd-bd=0 / ref=5 / no-allow-non-conformance / no-repeat-headers / annexb / no-aud / no-hrd / info / hash=0 / no-temporal-layers / no-open-gop / min-keyint=1 / keyint=360 / gop-lookahead=0 / bframes=4 / b-adapt=2 / b-pyramid / bframe-bias=0 / rc-lookahead=20 / lookahead-slices=4 / scenecut=40 / hist-scenecut=0 / radl=0 / no-splice / no-intra-refresh / ctu=64 / min-cu-size=8 / no-rect / no-amp / max-tu-size=32 / tu-inter-depth=1 / tu-intra-depth=1 / limit-tu=0 / rdoq-level=0 / dynamic-rd=0.00 / no-ssim-rd / signhide / no-tskip / nr-intra=0 / nr-inter=0 / no-constrained-intra / strong-intra-smoothing / max-merge=2 / limit-refs=3 / no-limit-modes / me=1 / subme=3 / merange=16 / temporal-mvp / no-frame-dup / no-hme / weightp / no-weightb / no-analyze-src-pics / no-deblock / sao / no-sao-non-deblock / rd=3 / selective-sao=4 / no-early-skip / rskip / no-fast-intra / no-tskip-fast / no-cu-lossless / no-b-intra / no-splitrd-skip / rdpenalty=0 / psy-rd=0.20 / psy-rdoq=0.00 / no-rd-refine / no-lossless / cbqpoffs=0 / crqpoffs=0 / rc=crf / crf=26.0 / qcomp=0.60 / qpstep=4 / stats-write=0 / stats-read=0 / ipratio=1.40 / pbratio=1.00 / aq-mode=0 / aq-strength=0.00 / no-cutree / zone-count=0 / no-strict-cbr / qg-size=64 / no-rc-grain / qpmax=51 / qpmin=0 / no-const-vbv / sar=1 / overscan=0 / videoformat=5 / range=0 / colorprim=2 / transfer=2 / colormatrix=2 / chromaloc=0 / display-window=0 / cll=0,0 / min-luma=0 / max-luma=1023 / log2-max-poc-lsb=8 / vui-timing-info / vui-hrd-info / slices=1 / no-opt-qp-pps / no-opt-ref-list-length-pps / no-multi-pass-opt-rps / scenecut-bias=0.05 / hist-threshold=0.03 / no-opt-cu-delta-qp / no-aq-motion / no-hdr10 / no-hdr10-opt / no-dhdr10-opt / no-idr-recovery-sei / analysis-reuse-level=0 / analysis-save-reuse-level=0 / analysis-load-reuse-level=0 / scale-factor=0 / refine-intra=0 / refine-inter=0 / refine-mv=1 / refine-ctu-distortion=0 / no-limit-sao / ctu-info=0 / no-lowpass-dct / refine-analysis-type=0 / copy-pic=1 / max-ausize-factor=1.0 / no-dynamic-refine / no-single-sei / no-hevc-aq / no-svt / no-field / qp-adaptation-range=1.00 / no-scenecut-aware-qpconformance-window-offsets / right=0 / bottom=0 / decoder-max-rate=0 / no-vbv-live-multi-pass
Language                    : English
Default                     : Yes
Forced                      : No
Color range                 : Limited


  


  I have tried with simplest ffmpeg from official wedsite and encoding cli but the output still same.

  


  ffmpeg -i "input.mkv" -c:v libx265 "output.mkv"


  


  Update :
Tried converting video with FFmpeg 4.4 "Rao" from https://www.ffmpeg.org/ working fine. But compiled ffmpeg from media-autobuild_suite, tried with light build and full build, Writing library and Encoding settings output still incorrect.

  


• Clickstream Data : Definition, Use Cases, and More

  15 avril 2024, par Erin

  Gaining a deeper understanding of user behaviour — customers’ different paths, digital footprints, and engagement patterns — is crucial for providing a personalised experience and making informed marketing decisions. 

  In that sense, clickstream data, or a comprehensive record of a user’s online activities, is one of the most valuable sources of actionable insights into users’ behavioural patterns. 

  This article will cover everything marketing teams need to know about clickstream data, from the basic definition and examples to benefits, use cases, and best practices. 

  What is clickstream data ? 

  As a form of web analytics, clickstream data focuses on tracking and analysing a user’s online activity. These digital breadcrumbs offer insights into the websites the user has visited, the pages they viewed, how much time they spent on a page, and where they went next.

  

  Your clickstream pipeline can be viewed as a “roadmap” that can help you recognise consistent patterns in how users navigate your website. 

  With that said, you won’t be able to learn much by analysing clickstream data collected from one user’s session. However, a proper analysis of large clickstream datasets can provide a wealth of information about consumers’ online behaviours and trends — which marketing teams can use to make informed decisions and optimise their digital marketing strategy. 

  Clickstream data collection can serve numerous purposes, but the main goal remains the same — gaining valuable insights into visitors’ behaviours and online activities to deliver a better user experience and improve conversion likelihood. 

  Depending on the specific events you’re tracking, clickstream data can reveal the following : 

  • How visitors reach your website 
  • The terms they type into the search engine
  • The first page they land on
  • The most popular pages and sections of your website
  • The amount of time they spend on a page 
  • Which elements of the page they interact with, and in what sequence
  • The click path they take 
  • When they convert, cancel, or abandon their cart
  • Where the user goes once they leave your website

  As you can tell, once you start collecting this type of data, you’ll learn quite a bit about the user’s online journey and the different ways they engage with your website — all without including any personal details about your visitors.

  Types of clickstream data 

  While all clickstream data keeps a record of the interactions that occur while the user is navigating a website or a mobile application — or any other digital platform — it can be divided into two types : 

  • Aggregated (web traffic) data provides comprehensive insights into the total number of visits and user interactions on a digital platform — such as your website — within a given timeframe 
  • Unaggregated data is broken up into smaller segments, focusing on an individual user’s online behaviour and website interactions 

  One thing to remember is that to gain valuable insights into user behaviour and uncover sequential patterns, you need a powerful tool and access to full clickstream datasets. Matomo’s Event Tracking can provide a comprehensive view of user interactions on your website or mobile app — everything from clicking a button and completing a form to adding (or removing) products from their cart. 

  On that note, based on the specific events you’re tracking when a user visits your website, clickstream data can include : 

  • Web navigation data : referring URL, visited pages, click path, and exit page
  • User interaction data : mouse movements, click rate, scroll depth, and button clicks
  • Conversion data : form submissions, sign-ups, and transactions 
  • Temporal data : page load time, timestamps, and the date and time of day of the user’s last login 
  • Session data : duration, start, and end times and number of pages viewed per session
  • Error data : 404 errors and network or server response issues 

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  Clickstream data benefits and use cases 

  Given the actionable insights that clickstream data collection provides, it can serve a wide range of use cases — from identifying behavioural patterns and trends and examining competitors’ performance to helping marketing teams map out customer journeys and improve ROI.

  

  According to the global Clickstream Analytics Market Report 2024, some key applications of clickstream analytics include click-path optimisation, website and app optimisation, customer analysis, basket analysis, personalisation, and traffic analysis. 

  

  The behavioural patterns and user preferences revealed by clickstream analytics data can have many applications — we’ve outlined the prominent use cases below. 

  Customer journey mapping 

  Clickstream data allows you to analyse the e-commerce customer’s online journey and provides insights into how they navigate your website. With such a comprehensive view of their click path, it becomes easier to understand user behaviour at each stage — from initial awareness to conversion — identify the most effective touchpoints and fine-tune that journey to improve their conversion likelihood. 

  Identifying customer trends 

  Clickstream data analytics can also help you identify trends and behavioural patterns — the most common sequences and similarities in how users reached your website and interacted with it — especially when you can access data from many website visitors. 

  Think about it — there are many ways in which you can use these insights into the sequence of clicks and interactions and recurring patterns to your team’s advantage. 

  Here’s an example : 

  It can reveal that some pieces of content and CTAs are performing well in encouraging visitors to take action — which shows how you should optimise other pages and what you should strive to create in the future, too. 

  Preventing site abandonment 

  Cart abandonment remains a serious issue for online retailers : 

  According to a recent report, the global cart abandonment rate in the fourth quarter of 2023 was at 83%. 

  That means that roughly eight out of ten e-commerce customers will abandon their shopping carts — most commonly due to additional costs, slow website loading times and the requirement to create an account before purchasing. 

  In addition to cart abandonment predictions, clickstream data analytics can reveal the pages where most visitors tend to leave your website. These drop-off points are clear indicators that something’s not working as it should — and once you can pinpoint them, you’ll be able to address the issue and increase conversion likelihood.

  Improving marketing campaign ROI 

  As previously mentioned, clickstream data analysis provides insights into the customer journey. Still, you may not realise that you can also use this data to keep track of your marketing effectiveness. 

  Global digital ad spending continues to grow — and is expected to reach $836 billion by 2026. It’s easy to see why relying on accurate data is crucial when deciding which marketing channels to invest in. 

  You want to ensure you’re allocating your digital marketing and advertising budget to the channels — be it SEO, pay-per-click (PPC) ads, or social media campaigns — that impact driving conversions. 

  When you combine clickstream e-commerce data with conversion rates, you’ll find the latter in Matomo’s goal reports and have a solid, data-driven foundation for making better marketing decisions.

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  Get the web insights you need, without compromising data accuracy.

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  Delivering a better user experience (UX) 

  Clickstream data analysis allows you to identify specific “pain points” — areas of the website that are difficult to use and may cause customer frustration. 

  It’s clear how this would be beneficial to your business : 

  Once you’ve identified these pain points, you can make the necessary changes to your website’s layout and address any technical issues that users might face, improving usability and delivering a smoother experience to potential customers. 

  Collecting clickstream data : Tools and legal implications 

  Your team will need a powerful tool capable of handling clickstream analytics to reap the benefits we’ve discussed previously. But at the same time, you need to respect users’ online privacy throughout clickstream data collection.

  

  Generally speaking, there are two ways to collect data about users’ online activity — web analytics tools and server log files.

  Web analytics tools are the more commonly used solution. Specifically designed to collect and analyse website data, these tools rely on JavaScript tags that run in the browser, providing actionable insights about user behaviour. Server log files can be a gold mine of data, too — but that data is raw and unfiltered, making it much more challenging to interpret and analyse. 

  That brings us to one of the major clickstream challenges to keep in mind as you move forward — compliance.

  While Google remains a dominant player in the web analytics market, there’s one area where Matomo has a significant advantage — user privacy. 

  Matomo operates according to privacy laws — including the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), making it an ethical alternative to Google Analytics. 

  It should go without saying, but compliance with data privacy laws — the most talked-about one being the GDPR framework introduced by the EU — isn’t something you can afford to overlook. 

  The GDPR was first implemented in the EU in 2018. Since then, several fines have been issued for non-compliance — including the record fine of €1.2 billion that Meta Platforms, Inc. received in 2023 for transferring personal data of EU-based users to the US.

  Clickstream analytics data best practices 

  

  As valuable as it might be, processing large amounts of clickstream analytics data can be a complex — and, at times, overwhelming — process. 

  Here are some best practices to keep in mind when it comes to clickstream analysis : 

  Define your goals 

  It’s essential to take the time to define your goals and objectives. 

  Once you have a clear idea of what you want to learn from a given clickstream dataset and the outcomes you hope to see, it’ll be easier to narrow down your scope — rather than trying to tackle everything at once — before moving further down the clickstream pipeline. 

  Here are a few examples of goals and objectives you can set for clickstream analysis : 

  • Understanding and predicting users’ behavioural patterns 
  • Optimising marketing campaigns and ROI 
  • Attributing conversions to specific marketing touchpoints and channels

  Analyse your data 

  Collecting clickstream analytics data is only part of the equation ; what you do with raw data and how you analyse it matters. You can have the most comprehensive dataset at your disposal — but it’ll be practically worthless if you don’t have the skill set to analyse and interpret it. 

  In short, this is the stage of your clickstream pipeline where you uncover common sequences and consistent patterns in user behaviour. 

  Clickstream data analytics can extract actionable insights from large datasets using various approaches, models, and techniques. 

  Here are a few examples : 

  • If you’re working with clickstream e-commerce data, you should perform funnel or conversion analyses to track conversion rates as users move through your sales funnel. 
  • If you want to group and analyse users based on shared characteristics, you can use Matomo for cohort analysis. 
  • If your goal is to predict future trends and outcomes — conversion and cart abandonment prediction, for example — based on available data, prioritise predictive analytics.

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  Organise and visualise your data

  As you reach the end of your clickstream pipeline, you need to start thinking about how you will present and communicate your data. And what better way to do that than to transform that data into easy-to-understand visualisations ? 

  Here are a few examples of easily digestible formats that facilitate quick decision-making : 

  • User journey maps, which illustrate the exact sequence of interactions and user flow through your website 
  • Heatmaps, which serve as graphical — and typically colour-coded — representations of a website visitor’s activity 
  • Funnel analysis, which are broader at the top but get increasingly narrower towards the bottom as users flow through and drop off at different stages of the pipeline 

  Collect clickstream data with Matomo 

  Clickstream data is hard to beat when tracking the website visitor’s journey — from first to last interaction — and understanding user behaviour. By providing real-time insights, your clickstream pipeline can help you see the big picture, stay ahead of the curve and make informed decisions about your marketing efforts. 

  Matomo accurate data and compliance with GDPR and other data privacy regulations — it’s an all-in-one, ethical platform that can meet all your web analytics needs. That’s why over 1 million websites use Matomo for their web analytics.

  Try Matomo free for 21 days. No credit card required.

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