Basic Usage

Vectorly's Background Filter takes as an input any MediaStream or MediaStreamTrack element, so for a WebRTC application, all you need to do is to instantiate the filter object with the MediaStream or MediaStreamTrack element you want to filter. The output is a MediaStreamTrack, which can be sent via WebRTC or loaded locally into a video element

The basic API for loading the background filter is:

   const stream = await navigator.mediaDevices.getUserMedia({video:true, audio:true});
   const filter = new BackgroundFilter(stream, {token: 'vectorly-token', background: 'blur'});
   const outputStream =  await filter.getOutput();
					

The above example is for a Background Blur filter. For virtual backgrounds, where you replace the background with an image, the API is:

   const filter = new BackgroundFilter(stream, {token: 'vectorly-token', background: 'my-image-url.png'});
					

Integration

Integrating the filter with any specific Video Conferencing API or service just requires finding the MediaStream element associated with video stream you want to filter. The following sub-sections discuss how to integrate the filter with various conferencing services.

Vanilla WebRTC

As shown above, the API for basic/general WebRTC is:

   navigator.mediaDevices.getUserMedia({video:true, audio:true}).then(function(stream) {

      const filter = new BackgroundFilter(stream, {token: 'insert-vectorly-token-here', background: 'blur'});
      filter.getOutput().then(function(filteredStream){
            //Video Stream Track
      });
   });

You can find a demo repository for vanilla WebRTC background filtering here

Jitsi

You can enable filters on any VideoTrack object by feeding into the Filter's Jitsi Plugin (see reference).

   const room = connection.initJitsiConference('conference', confOptions);

   JitsiMeetJS.createLocalTracks({devices: ['video']}).then(tracks => {
      const filter = new BackgroundFilter(tracks[0], {token: 'insert-vectorly-token', background: 'blur'});

	filter.getOutput().then(function(filteredStream){
            room.addTrack(filteredStream);
        });

   });

Agora

For Web deployments using Agora (specifically the 4.x API), you can just feed the video track to the Background filter, which will return a filtered video track which you can publish.

    let videoTrack = AgoraRTC.createCameraVideoTrack();
    let audioTrack = AgoraRTC.createMicrophoneAudioTrack();

    const inputStream = new MediaStream([videoTrack._mediaStreamTrack]);


    const filter = new BackgroundFilter(inputStream, {token: 'insert-vectorly-token-here', background: 'blur'});
    filter.getOutput().then(function(filteredTrack ){

       const filteredAgoraTrack = AgoraRTC.createCustomVideoTrack({
         mediaStreamTrack: filteredStream.getVideoTracks()[0],
       });
      client.publish([filteredAgoraTrack, audioTrack]);
    });

						

You can find a working demo repo here

Twilio

You can enable filters on any VideoTrack object by extracting the raw MediaStreamTrack, running the filter on that, and creating a new LocalVideoTrack.

   const Twilio = require('twilio-video');

   const localVideoTrack = await Twilio.createLocalVideoTrack();
   const inputStream = new MediaStream([videoTrack.mediaStreamTrack]);
   const filter = new BackgroundFilter(inputStream, {token: 'insert-vectorly-token', background: 'blur'});
   const outputStream = await filter.getOutput();
   const filteredTrack = new Twilio.LocalVideoTrack(outputStream.getVideoTracks()[0]);

   room.localParticipant.publishTrack(filteredTrack);

							

Vonage / OpenTok

When you create a Publisher object, just feed the filtered video Track as the video Source

   const stream = await navigator.mediaDevices.getUserMedia({video:true, audio:true});
   const filter = new BackgroundFilter(stream, {token: 'vectorly-token', background: 'blur'});
   const outputStream =  await filter.getOutput();

   var publisher = OT.initPublisher('publisher', {
      insertMode: 'append',
      width: '100%',
      height: '100%',
      videoSource: outputStream.getVideoTracks()[0]
   }, handleError);
							

You can find a working demo repo here

Integration

General WebRTC

The vectorlyUpscaler works with any video tag, so for a WebRTC application, all you need to do is to instantiate the upscaler object with the video element you want to upscale.

   const upscaler  = new vectorlyUpscaler(document.getElementById("remoteVideo"), {token: 'insert-vectorly-token-here'});

We have an example repository, showing how Vectorly can be integrated with WebRTC, as well as a full working general WebRTC demo here.

Integrating the upscaler with any specific Video Conferencing API or service just requires finding the video element associated with video stream you want to upscale.

Jitsi

You can enable upscaling on any VideoTrack object by intercepting the corresponding video element you attach it to (see reference).

   const room = connection.initJitsiConference('conference', confOptions);
   room.on(JitsiMeetJS.events.conference.TRACK_ADDED, function(track){

      const videoElement = document.createElement('video');
      document.body.appendChild(videoElement);
      track.attach(videoElement);

      const upscaler = new vectorlyUpscaler(videoElement.current, {token: 'insert-vectorly-token'});

   });

Agora

For Web deployments using Agora, you can find the video element of the stream you want to upscale by using the stream's ID.

    let stream = AgoraRTC.createStream({
        streamID: uid,
        audio: true,
        video: true,
        screen: false
    });

    stream.init(function() {

        stream.play('target-div');
        const video = document.getElementById("video" + stream.getId());
        const upscaler = new vectorlyUpscaler(video, {token: 'insert-vectorly-token-here'});

        client.publish(stream);

    });
						

Twilio

You can enable upscaling on any VideoTrack object by intercepting the corresponding video element you attach it to (see reference).

If you use the track.attach() method to create a video element:

   const Video = require('twilio-video');

   Video.createLocalVideoTrack().then(function(videoTrack) {
     const videoElement = videoTrack.attach();
     document.body.appendChild(videoElement);
     const upscaler = new vectorlyUpscaler(videoElement.current, {token: 'insert-vectorly-token'});
   });
							

If you specify your own video element:

   const Video = require('twilio-video');

   const videoElement = document.createElement('video');
   document.body.appendChild(videoElement);

   Video.createLocalVideoTrack().then(function(videoTrack) {
     videoTrack.attach(videoElement);
     const upscaler = new vectorlyUpscaler(videoEl.current, {token: 'insert-vectorly-token'});
   });
							

OpenTok / Vonage

When a Subscriber or Publisher creates a video element you can intercept it and feed that video to the Vectorly Upscaler.

You can use the subscriber.element property to intercept the video element

    session.on('streamCreated', function(event) {

        const subscriber = session.subscribe(event.stream, 'subscriber', {
            insertMode: 'append',
            width: '100%',
            height: '100%'
        }, handleError);

        subscriber.on('videoElementCreated', function (){
            const video = subscriber.element.querySelector('video');
            const upscaler  = new vectorlyUpscaler(video, {token: '..your-token....'});

        });

    });
							

This also works with a publisher object. Refer to the Vonage documentation for styling - Vectorly's upscaler will fit within the styling defined by OpenTok.

See our example repistory for a working code example

Daily.co

You can integrate Vectorly's AI upscaler with Daily.co if you're building a custom custom video chat interface . Using the default React code sample from Daily, we've built a full working demo reference

   useEffect(() => {
	videoEl.current &&
	(videoEl.current.srcObject = new MediaStream([videoTrack]));
	if (videoEl.current && props.isLarge) {
	window.upscalers = window.upscalers || {}
	window.upscalers[videoTrack.id] = new vectorlyUpscaler(videoEl.current, {token: 'insert-vectorly-token'});
	}
   }, [videoTrack]);
							

You just need to make sure you intercept the video element associated with the video track you want to upscale.

Vectorly's AI upscaler is not compatible with the pre-built UI from Daily.co, as the pre-built UI is loaded via iframe, making it impossible to access the video element through a third party application.

Electron

If you're building an electron app, the Vectorly library is fairly plug and play, and will work with either CDN or NPM installation.

You can see a demo electron app repostory here

Mobile

Android

Our Android SDK works as a plugin to ExoPlayer. You will therefore need to use ExoPlayer, or an ExoPlayer derived player, in order upscale video.

First, you'll need to include our SDK into your app's gradle file. You can import it from our Maven repository, as shown below.

							 
   repositories {
     google()
     jcenter()
     maven { url "http://maven.vectorly.io/artifactory/libs-release-local" }
   }
   dependencies {
      implementation 'io.vectorly.glnnrender:glnnrender:0.1.1'
   }
							 
						 

You can then add the following imports into the activity which manages your ExoPlayer view

							 
   import io.vectorly.glnnrender.GlPlayerView;
   import io.vectorly.glnnrender.networks.NetworkTypes;
							 
						 

Once the ExoPlayer view is set up, you can call set up the Upscaler as in the following example. You'll need to feed your API key, which you can get from the Vectorly dashboard.

							 
   private GlPlayerView ePlayerView;

   private void setupUpscalerView() {

           String api_key = "...";
           ePlayerView = new GlPlayerView(this, api_key);
           ePlayerView.setSimpleExoPlayer(player);

           ePlayerView.setNetwork(NetworkTypes.DEFAULT, getApplicationContext());
            ePlayerView.setLayoutParams(new RelativeLayout.LayoutParams(ViewGroup.LayoutParams.MATCH_PARENT, ViewGroup.LayoutParams.MATCH_PARENT));
           ((MovieWrapperView) findViewById(R.id.layout_movie_wrapper)).addView(ePlayerView);
           ePlayerView.onResume();

    }

						 

We've created a full working example using our library, which you can find below

Example Repo

iOS

We plant to release an iOS SDK in Summer 2021

Background Segmentation

Our background filter is based on the Google Meet model, and run using Media Pipe the same way it is run in Google Meet. Google Meet's background-segmentation model runs entirely on the CPU, which does require performance considerations.

You can measure the fps on any given device by adding the analyticsEnabled flag as true in the configuration parameters.

							 
  const config = {
	   token: '...',
	   background: 'blur',
	   analyticsEnabled: true
   };

   const filter = BackgroundFilter(video, config);
							 
						 

Performance Considerations

We recommend only running the Web Background filters library for desktop users. The performance is considerably worse on Mobile because because the overhead of communicating between the browser and GPU is much higher on mobile devices.

We expect the performance to rival or exceed desktop clients with a Native Android SDK, as is the case for our AI Upscaler Android SDK. We are planning to develop a Background Filters Android and iOS SDK in Q3 2021.

Additionally, we are working on a fully WebGL/OpenGL implementation of the Background Filter, which should greatly exceed the performance of Google's Meet model.

AI Upscaling

The primary "cost" to doing super-resolution is computational complexity. While we have put a lot of work into making super resolution feasible on client devices, it is still something which needs to be managed. Here, we provide some initial performance benchmarks for the same demos shown above, in the demos sections.

Performance Considerations

AI Upscaling does require some computational effort, however it is mostly on the graphics card, so AI Upscaling's impact on CPU is limited. The amount of computation (and therefore the framerate / performance) depends on the size of input video you are upscaling

The following table should give a rough idea performance for different input video resolutions. These results are only for Web environments. Our mobile SDKs will have access to more powerful native libraries, enabling significantly better performance.

You can measure the fps on any given device by adding the analyticsEnabled flag as true in the configuration parameters.

							 
  const config = {
	   token: '...',
	   analyticsEnabled: true
   };

   const upscaler = vectorlyUpscaler(video, config);
							 
						 

You can then measure the fps at any time with upscaler.metrics.fps property. The fps number provided by upscaler.metrics.fps will not exceed the source video's frame rate because we only render when a video frame changes.

It's recommended to stick to 240p or 360p inputs, as mid-range devices tend to struggle with larger inputs. You can also disable upscaling if the fps gets too low.

Quality

The primary benefit of Super resolution is to increase video quality. Using the original high-resolution video as a reference, we can use traditional video quality metrics like VMAF to quantify the quality improvement of Super Resolution, when compared to normal bicubic upscaling of the downsampled / low-resolution video content.

Our general AI upscaler filter generally achieves a 10 to 15 point VMAF improvement compared to bicubic scaling. With content-specific AI models, or heavier models, we will likely be able to achieve further quality gains. We are currently working on releasing quality comparisons for content specific models.

Quality visualization

For reference, below are side by side comparisons of bicubic upscaling of the low-resolution original / Super resolution of the low-resolution / High resolution original

Jellyfish

Ducks

Tractor