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Changelog: - DXV DXT1 encoder - LEAD MCMP decoder - EVC decoding using external library libxevd - EVC encoding using external library libxeve - QOA decoder

by gyan 3y ago

Changelog: - DXV DXT1 encoder - LEAD MCMP decoder - EVC decoding using external library libxevd - EVC encoding using external library libxeve - QOA decoder and demuxer - aap filter - demuxing, decoding, filtering, encoding, and muxing in the - ffmpeg CLI now all run in parallel - enable gdigrab device to grab a window using the hwnd=HANDLER syntax - IAMF raw demuxer and muxer - D3D12VA hardware accelerated H264, HEVC, VP9, AV1, MPEG-2 and VC1 decoding - tiltandshift filter - qrencode filter and qrencodesrc source - quirc filter - lavu/eval: introduce randomi() function in expressions - VVC decoder (experimental) - fsync filter - Raw Captions with Time (RCWT) closed caption muxer - ffmpeg CLI -bsf option may now be used for input as well as output - ffmpeg CLI options may now be used as -/opt <path>, which is equivalent - to -opt <contents of file <path>> - showinfo bitstream filter - a C11-compliant compiler is now required; note that this requirement - will be bumped to C17 in the near future, so consider updating your - build environment if it lacks C17 support - Change the default bitrate control method from VBR to CQP for QSV encoders. - removed deprecated ffmpeg CLI options -psnr and -map_channel - DVD-Video demuxer, powered by libdvdnav and libdvdread - ffprobe -show_stream_groups option - ffprobe (with -export_side_data film_grain) now prints film grain metadata - AEA muxer - ffmpeg CLI loopback decoders - Support PacketTypeMetadata of PacketType in enhanced flv format - ffplay with hwaccel decoding support (depends on vulkan renderer via libplacebo) - dnn filter libtorch backend - Android content URIs protocol - AOMedia Film Grain Synthesis 1 (AFGS1) - RISC-V optimizations for AAC, FLAC, JPEG-2000, LPC, RV4.0, SVQ, VC1, VP8, and more - Loongarch optimizations for HEVC decoding - Important AArch64 optimizations for HEVC - IAMF support inside MP4/ISOBMFF - Support for HEIF/AVIF still images and tiled still images - Dolby Vision profile 10 support in AV1 - Support for Ambient Viewing Environment metadata in MP4/ISOBMFF - HDR10 metadata passthrough when encoding with libx264, libx265, and libsvtav1

deleted 3y ago
[deleted]
deskamess 3y ago
> - ffmpeg CLI now all run in parallel I think I read about this a few months ago but don't remember the details. What exactly does this do? Does it result in faster encoding/decoding if you have multiple filter graphs (for example a single cmd line that transcodes to new audio, extracts image, creates a low res) > - ffmpeg CLI loopback decoders No idea what this is... Edit: threading => https://ffmpeg.org//index.html#cli_threading https://ffmpeg.org//index.html#cli_threading, loopback => https://ffmpeg.org/ffmpeg.html#Loopback-decoders https://ffmpeg.org/ffmpeg.html#Loopback-decoders Loopback decoders are a nice concept. So could I use this to create a single ffmpeg command to extract images periodically (say 1/s) and then merge them into a horizontal strip (using the loopback decoder for this part)?
- gyan 3y ago
  You don't need a loopback decoder for that. The periodic extraction will depend on a filter, and you can just clone and send the output of that filter to the tiling filter.
  - deskamess 3y ago
    Had to go to ChatGPT for help. It appears that you need to know how many tiles to stitch. I was hoping to have that dynamically determined. Not sure if loopback will help. CGPT said: ffmpeg -i input.mp4 -vf "fps=1,tile=3x1" -frames:v 1 output_stitched.png Gemini: ffmpeg -i input_video.mp4 -vf "fps=1,scale=220:-1" -c:v png output.png
Avamander 3y ago
> - D3D12VA hardware accelerated H264, HEVC, VP9, AV1, MPEG-2 and VC1 decoding I wonder if this also means that Chrome and Edge will be able to use this acceleration for their ffmpeg backend (instead of relying on MediaFoundation)?
ttyprintk 3y ago
And also, thank you for packaging.
sorenjan 3y ago
First of, ffmpeg is amazing, I'm very thankful to everyone involved in it. > dnn filter libtorch backend What's ffmpeg's plan regarding ML based filters? When looking through the filter documentation it seems like filters use three different backends: tensorflow, torch, and openvino. Doesn't seem optimal, is there any discussion about consolidating on one backend? ML filters need model files, and the filters take a path to a model file as one of their arguments. This makes them really difficult to use, if you're lucky you can find a suitable model and download somewhere, otherwise you need to find a separate model training project and dataset and run that first. Are there any plans on streamlining ML filters and model handling for ffmpeg? Maybe a model file repository with an option of installing these in an official models path on the system? Most image and video research use ML now, but I don't get the impression that ffmpeg tries to integrate the modern technologies well yet. Being able to do for instance spatial and temporal super resolution using standard ffmpeg filters would be a big improvement, and I think things like automatic subtitles using whisper would be a good fit too. But it should start with a coherent ML strategy regarding inference backend and model management.

by gyan 3y ago

deleted 3y ago

[deleted]

deskamess 3y ago

> - ffmpeg CLI now all run in parallel I think I read about this a few months ago but don't remember the details. What exactly does this do? Does it result in faster encoding/decoding if you have multiple filter graphs (for example a single cmd line that transcodes to new audio, extracts image, creates a low res) > - ffmpeg CLI loopback decoders No idea what this is... Edit: threading => https://ffmpeg.org//index.html#cli_threading https://ffmpeg.org//index.html#cli_threading, loopback => https://ffmpeg.org/ffmpeg.html#Loopback-decoders https://ffmpeg.org/ffmpeg.html#Loopback-decoders Loopback decoders are a nice concept. So could I use this to create a single ffmpeg command to extract images periodically (say 1/s) and then merge them into a horizontal strip (using the loopback decoder for this part)?

gyan 3y ago
You don't need a loopback decoder for that. The periodic extraction will depend on a filter, and you can just clone and send the output of that filter to the tiling filter.
- deskamess 3y ago
  Had to go to ChatGPT for help. It appears that you need to know how many tiles to stitch. I was hoping to have that dynamically determined. Not sure if loopback will help. CGPT said: ffmpeg -i input.mp4 -vf "fps=1,tile=3x1" -frames:v 1 output_stitched.png Gemini: ffmpeg -i input_video.mp4 -vf "fps=1,scale=220:-1" -c:v png output.png

Avamander 3y ago

> - D3D12VA hardware accelerated H264, HEVC, VP9, AV1, MPEG-2 and VC1 decoding I wonder if this also means that Chrome and Edge will be able to use this acceleration for their ffmpeg backend (instead of relying on MediaFoundation)?

ttyprintk 3y ago

And also, thank you for packaging.

sorenjan 3y ago

First of, ffmpeg is amazing, I'm very thankful to everyone involved in it. > dnn filter libtorch backend What's ffmpeg's plan regarding ML based filters? When looking through the filter documentation it seems like filters use three different backends: tensorflow, torch, and openvino. Doesn't seem optimal, is there any discussion about consolidating on one backend? ML filters need model files, and the filters take a path to a model file as one of their arguments. This makes them really difficult to use, if you're lucky you can find a suitable model and download somewhere, otherwise you need to find a separate model training project and dataset and run that first. Are there any plans on streamlining ML filters and model handling for ffmpeg? Maybe a model file repository with an option of installing these in an official models path on the system? Most image and video research use ML now, but I don't get the impression that ffmpeg tries to integrate the modern technologies well yet. Being able to do for instance spatial and temporal super resolution using standard ffmpeg filters would be a big improvement, and I think things like automatic subtitles using whisper would be a good fit too. But it should start with a coherent ML strategy regarding inference backend and model management.