Posted by Nevin Mital – Developer Relations Engineer, and Kristina Simakova – Engineering Supervisor
Android customers have demonstrated an rising need to create, personalize, and share video content material on-line, whether or not to protect their reminiscences or to make folks snicker. As such, media enhancing is a cornerstone of many partaking Android apps, and traditionally builders have usually relied on exterior libraries to deal with operations akin to Trimming and Resizing. Whereas these options are highly effective, integrating and managing exterior library dependencies can introduce complexity and result in challenges with managing efficiency and high quality.
The Jetpack Media3 Transformer APIs supply a local Android answer that streamline media enhancing with quick efficiency, in depth customizability, and broad machine compatibility. On this weblog publish, we’ll stroll by way of a number of the commonest enhancing operations with Transformer and focus on its efficiency.
Getting arrange with Transformer
To get began with Transformer, try our Getting Began documentation for particulars on how you can add the dependency to your mission and a fundamental understanding of the workflow when utilizing Transformer. In a nutshell, you’ll:
- Create one or many MediaItem cases out of your video file(s), then
- Apply item-specific edits to them by constructing an EditedMediaItem for every MediaItem,
- Create a Transformer occasion configured with settings relevant to the entire exported video,
- and at last begin the export to avoid wasting your utilized edits to a file.
Apart: You can too use a CompositionPlayer to preview your edits earlier than exporting, however that is out of scope for this weblog publish, as this API continues to be a piece in progress. Please keep tuned for a future publish!
Right here’s what this seems like in code:
val mediaItem = MediaItem.Builder().setUri(mediaItemUri).construct() val editedMediaItem = EditedMediaItem.Builder(mediaItem).construct() val transformer = Transformer.Builder(context) .addListener(/* Add a Transformer.Listener occasion right here for completion occasions */) .construct() transformer.begin(editedMediaItem, outputFilePath)
Transcoding, Trimming, Muting, and Resizing with the Transformer API
Let’s now check out 4 of the most typical single-asset media enhancing operations, beginning with Transcoding.
Transcoding is the method of re-encoding an enter file right into a specified output format. For this instance, we’ll request the output to have video in HEVC (H265) and audio in AAC. Beginning with the code above, listed here are the traces that change:
val transformer =
Transformer.Builder(context)
.addListener(...)
.setVideoMimeType(MimeTypes.VIDEO_H265)
.setAudioMimeType(MimeTypes.AUDIO_AAC)
.construct()
Lots of you could already be acquainted with FFmpeg, a preferred open-source library for processing media information, so we’ll additionally embody FFmpeg instructions for every instance to function a useful reference. Right here’s how one can carry out the identical transcoding with FFmpeg:
$ ffmpeg -i $inputVideoPath -c:v libx265 -c:a aac $outputFilePath
The following operation we’ll strive is Trimming.
Particularly, we’ll set Transformer as much as trim the enter video from the three second mark to the 8 second mark, leading to a 5 second output video. Beginning once more from the code within the “Getting arrange” part above, listed here are the traces that change:
// Configure the trim operation by including a ClippingConfiguration to // the media merchandise val clippingConfiguration = MediaItem.ClippingConfiguration.Builder() .setStartPositionMs(3000) .setEndPositionMs(8000) .construct() val mediaItem = MediaItem.Builder() .setUri(mediaItemUri) .setClippingConfiguration(clippingConfiguration) .construct() // Transformer additionally has a trim optimization function we will allow. // This may prioritize Transmuxing over Transcoding the place doable. // See extra about Transmuxing additional down on this publish. val transformer = Transformer.Builder(context) .addListener(...) .experimentalSetTrimOptimizationEnabled(true) .construct()
With FFmpeg:
$ ffmpeg -ss 00:00:03 -i $inputVideoPath -t 00:00:05 $outputFilePath
Subsequent, we will mute the audio within the exported video file.
val editedMediaItem = EditedMediaItem.Builder(mediaItem) .setRemoveAudio(true) .construct()
The corresponding FFmpeg command:
$ ffmpeg -i $inputVideoPath -c copy -an $outputFilePath
And for our ultimate instance, we’ll strive resizing the enter video by scaling it right down to half its unique peak and width.
val scaleEffect = ScaleAndRotateTransformation.Builder() .setScale(0.5f, 0.5f) .construct() val editedMediaItem = EditedMediaItem.Builder(mediaItem) .setEffects( /* audio */ Results(emptyList(), /* video */ listOf(scaleEffect)) ) .construct()
An FFmpeg command might appear like this:
$ ffmpeg -i $inputVideoPath -filter:v scale=w=trunc(iw/4)*2:h=trunc(ih/4)*2 $outputFilePath
In fact, you can even mix these operations to use a number of edits on the identical video, however hopefully these examples serve to reveal that the Transformer APIs make configuring these edits easy.
Transformer API Efficiency outcomes
Listed below are some benchmarking measurements for every of the 4 operations taken with the Stopwatch API, operating on a Pixel 9 Professional XL machine:
(Be aware that efficiency for operations like these can rely on quite a lot of causes, akin to the present load the machine is below, so the numbers under must be taken as tough estimates.)
Enter video format: 10s 720p H264 video with AAC audio
- Transcoding to H265 video and AAC audio: ~1300ms
- Trimming video to 00:03-00:08: ~2300ms
- Muting audio: ~200ms
- Resizing video to half peak and width: ~1200ms
Enter video format: 25s 360p VP8 video with Vorbis audio
- Transcoding to H265 video and AAC audio: ~3400ms
- Trimming video to 00:03-00:08: ~1700ms
- Muting audio: ~1600ms
- Resizing video to half peak and width: ~4800ms
Enter video format: 4s 8k H265 video with AAC audio
- Transcoding to H265 video and AAC audio: ~2300ms
- Trimming video to 00:03-00:08: ~1800ms
- Muting audio: ~2000ms
- Resizing video to half peak and width: ~3700ms
One method Transformer makes use of to hurry up enhancing operations is by prioritizing transmuxing for fundamental video edits the place doable. Transmuxing refers back to the means of repackaging video streams with out re-encoding, which ensures high-quality output and considerably sooner processing occasions.
When not doable, Transformer falls again to transcoding, a course of that entails first decoding video samples into uncooked information, then re-encoding them for storage in a brand new container. Listed below are a few of these variations:
Transmuxing
- Transformer’s most well-liked strategy when doable – a fast transformation that preserves elementary streams.
- Solely relevant to fundamental operations, akin to rotating, trimming, or container conversion.
- No high quality loss or bitrate change.
Transcoding
- Transformer’s fallback strategy in circumstances when Transmuxing is not doable – Includes decoding and re-encoding elementary streams.
- Extra in depth modifications to the enter video are doable.
- Loss in high quality as a result of re-encoding, however can obtain a desired bitrate goal.
We’re repeatedly implementing additional optimizations, such because the not too long ago launched experimentalSetTrimOptimizationEnabled setting that we used within the Trimming instance above.
A trim is often carried out by re-encoding all of the samples within the file, however since encoded media samples are saved chronologically of their container, we will enhance effectivity by solely re-encoding the group of images (GOP) between the beginning level of the trim and the primary keyframes at/after the beginning level, then stream-copying the remainder.
Since we solely decode and encode a set portion of any file, the encoding latency is roughly fixed, no matter what the enter video period is. For lengthy movies, this improved latency is dramatic. The optimization depends on having the ability to sew a part of the enter file with newly-encoded output, which implies that the encoder’s output format and the enter format should be appropriate.
If the optimization fails, Transformer robotically falls again to regular export.
What’s subsequent?
As a part of Media3, Transformer is a local answer with low integration complexity, is examined on and ensures compatibility with all kinds of units, and is customizable to suit your particular wants.
To dive deeper, you’ll be able to discover Media3 Transformer documentation, run our pattern apps, or discover ways to complement your media enhancing pipeline with Jetpack Media3. We’ve already seen app builders profit significantly from adopting Transformer, so we encourage you to strive them out your self to streamline your media enhancing workflows and improve your app’s efficiency!