Delegated Upload

Video Status

Video tagging

Video list

Video update

Normal

doug.jpg

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Doug Twitter

Doug Sillars

Tutorials

NodeJS

JavaScript

# Video Content Moderation

User generated content (UGC) is taking over the internet. From sites offering video product reviews to vlogging and online education - video is being created at the fastest pace yet (and shows no sign of stopping). Many companies are looking for ways to easily incorporate UGC video on their sites.  

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All of our customers who allow UGC to be posted on their site worry about a few bad actors working to ruin their brand by posting inappropriate content. In order to protect their brand and the content on their site - evaluating any user generated content before it is allowed to be placed on the website is an important step. Traditional content moderation requires human moderators (which takes time and is expensive).

In this post, I'll walk through an alternative: using machine learning to moderate videos. Before publishing, it is scanned using machine learning for content, tested against rules, and then either accepted or rejected based on those rules.  Super fast, works 24 hours a day, and there will be no human error in the categorisation of the video.

As a demonstration, we've built a site with [api.video](https://api.video) for video hosting, and using Hive AI to power the video moderation.

## The Basics

## api.video
api.video is a full service API-based video hosting solution.  You can use our APIs to upload videos, modify and serve streaming video.  Every video that is uploaded is transcoded into a video stream, and can be delivered in a custom video player. If you haven't created an api.video account yet, you can do that [here](https://dashboard.api.video/register). 

In this solution, we'll use the [delegated upload](https://docs.api.video/reference/api/Videos) (used like a public key) for uploading the videos.  We'll use the video [tagging](https://api.video/blog/tutorials/video-tagging-best-practices/) function to label each video based on the moderation results. We can search all of the tagged videos to deliver the videos (and custom player) to users based on their moderation state.

### Hive AI

Once the videos are uploaded, we need to run moderation before allowing them to appear on the website.  

Hive AI has several moderation suites. We've set up our API to use 2 endpoints. For short videos (under 25s) - we scan 1 frame every second, and for long videos, one frame every 5 seconds. (The demo above only works for 25s long videos).

The API is trained to identify several subjects that, depending on the context of your application, might be important to moderate. In this post, we will use a small subset of the trained models:

* Safe for work/Not Safe for Work
* Yes/No: Female nudity
* Yes/No: Male nudity
* Yes/No: Female swimwear
* Yes/No: Shirtless male
* Yes/No: guns
* Yes/No: smoking

After each frame is analyzed, a JSON file with data for each frame analyzed is returned for analysis.  

After analysis, the video is tagged with moderation value, ensuring that it only appears on pages appropriate for the video.

### Code

This application is available on [GitHub](https://github.com/apivideo/videoModeration). The general flow is:

1. On Upload, a video is tagged "needs moderation" (and will not appear on the site).
2. Hive AI performs frame analysis.
3. Based on the analysis, the video is tagged as "SFW" or "NSFW" (etc.).

moderation workflow, ai moderator, automated moderator

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On entering the site, users are presented with an upload form.  As we walk through this example, we'll be following the upload of the intro theme to the classic TV show Baywatch.

With api.video, we have created a delegated upload key - which allows us to place the code publicly on the webpage without exposing our API private key. The form takes the video, and uploads the video to api.video.  The uploader uses the [Blob API](https://api.video/blog/tutorials/uploading-large-files-with-javascript/) to break the videos into 50MB segments for uploading.

For the purposes of the demo, we show how many chunks are created, and update the progress of each chunk, in addition to the total video upload:


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The response from the upload provides the api.video ```videoId```, which is used to identify the video at api.video.  This is then sent to the NodeJS backend as a POST (along with the video's name).

On the Node server, we begin the process of moderating the video.  First, we call the [update video](https://docs.api.video/reference/api/Videos#update-a-video-object) endpoint to add the video's name and to tag the video "needsScreening" to indicate that it has entered the moderation queue.

### Transcoding

When the video is uploaded, api.video's servers begin the process of creating different size/bitrate videos to provide adaptive bitrate streaming.  We also create a mp4 version of the video. For content moderation, we need to submit the mp4 to Hive AI. The Node server pings api.video's [video status](https://docs.api.video/reference/api/Videos#retrieve-video-status-and-details) endpoint every 2 seconds to determine when the mp4 is ready. Initially, the API will indicate that the video is not playable (transcoding has not yet started). Once transcoding has started, the API lists the encoding status of every format being created, so we can monitor the encoding status of the mp4.

video status, video upload, upload a video

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Once the mp4 is created, we can create our connection to Hive AI, and make the request for content moderation.

The request looks like this:
```JSON
{
  method: 'POST',
  url: 'https://api.thehive.ai/api/v2/task/sync',
  headers: {
    accept: 'application/json',
    authorization: 'token {API TOKEN}'
  },
  form: {
    image_url: 'https://cdn.api.video/vod/vi1iHWIy6Doy0LBJl3ajaED0/mp4/1080/source.mp4'
  }
}
```

and a few seconds later, a huge JSON response comes back - 39 categories * x frames analysed.  Let's look at a snip of one frame to see the sort of information we get (for brevity, I've only included the first 5 categories that are returned):

```JSON
	{
		"class": "general_not_nsfw_not_suggestive",
		"score": 0.00460230773187999
	},
	{
		"class": "general_nsfw",
		"score": 5.180850871024288e-06
	},
	{
		"class": "general_suggestive",
		"score": 0.995392511417249
	},
	{
		"class": "no_female_underwear",
		"score": 0.9998768576722025
	},
	{
		"class": "yes_female_underwear",
		"score": 0.00012314232779748514
	}

```

"Not NSFW" means "Not Not Safe for Work" which, removing the double negative is "safe for work." This score is combined with "not suggestive", and is scored at 0.004.  Since 0 is a low score, and 1 is a high score, this means that the API has determined that this frame is not considered appropriate for work.  Looking at the next 2 values, general NSFW is also very small, but the "general suggestive" is 99.9%. Since the yes:no scores will always add up to one, this means that the general suggestiveness is what makes this frame not safe for work.

video moderator, automated content moderator, ai moderation

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### Compiling the scores

Hive AI gives you scores for each category on each frame, but it is up to us to define the pass/fail criteria for our videos.  

In order to do this, I take all the scores for each category, and place them in an array. With the data sequestered for each category, I can calculate the min, max, average and median of each score. I also calculate how many frames appear over 0.9 as a 90% certainty ("yes_smoking") and how many frames appear under 0.1 (90% certainty of "no_smoking.")  


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In the above "SFW" array (really the 'not not safe for work' response, but that is a mouthful), of 22 frames, I finde:
* min score: 0 (not safe for work)
* max score: 1 (safe)
* average:  0.55 right in the middle!
* median: 0.75 
* count of frames > 0.9: 9
* count of frames <0.1: 7

As you can see, 9 frames are over 0.9 (safe!), but 7 are below 0.1 (not safe!). If the median falls below 0.9, than means at least 50% of the frames are not "certain" to be safe for work ( which I place at the 90% threshold).  Based on these numbers, my rudimentary pass/fail algorithm deems the Baywatch intro "NSFW."  

If you wanted to prevent videos with smoking from appearing to your audience, just one frame with a 90% certainty of smoking would be enough to cause the video to be categorised as "yes_smoking." I used this same threshold for guns, nudity, underwear and swimwear. 

>Interestigly, this does push the Schindler's List trailer into the "yes smoking" category.

Based on these metrics, it is not terribly surprising that the Baywatch video is flagged for "yes female swimwear" and "yes shirtless male."  Also unsurprisingly, the algorithm found an absence of smoking and guns.

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Once we have the categories measured for each application, we can remove the tag ```needsScreening``` and add in the new tags from the moderation. This is done with the [Video Update](https://docs.api.video/reference/api/Videos#update-a-video-object) endpoint.  

And that ends the moderation process for the video.

### Video Categories

Now that each video has been categorised, it is easy to display each video category. Each video's categories have been added to the video as a tag and the [List videos](https://docs.api.video/reference/api/Videos#list-all-video-objects) endpoint allows us to search by tag - returning every video that does not have smoking, or is safe for work, etc.  Using Node, we can generate the list of videos (sorted newest first), and send them to the client:

```Javascript
app.get('/no_smoking', (req, res) => {
	//get list of no smoking videos
	client = new apiVideo.Client({ apiKey: apiVideoKey});
	let recordedList = client.videos.search({"tags":'no_smoking', "sortBy":"publishedAt","sortOrder":"desc"});
	recordedList.then(function(list) {
		console.log("list of tagged videos");
		console.log(list);
		return res.render('videos',{list});  
	}).catch((error) => {
	    console.log(error);
	});
		
});

```

The API returns the metadata for each video (including links for the video), so creating a page with each video iFrame is a pretty simple task.  On each page in the sample app, you can display (and watch) each of the videos assigned to a category.  In the demo, I use Pug for the rendering

```
 each video in list
    p #{video.title}
    iframe(type="text/html", src=video.assets.player, width = "960", height="540",frameborder="0", scrollling="no")
    p #{video.publishedAt}
   
```

For example, the "yes guns" page has 3 movie trailers (as I write this post): Indiana Jones and the Last Crusade, Die Hard and the latest James Bond,  

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## Conclusion

There we have it - we have uploaded a video to api.video, and before it is displayed on the site, it is moderated by Hive AI for several categories of inappropriateness.  Based on the analysis for each frame, the video is categorised into buckets and displayed on the appropriate page on the website.


If you have questions about using content moderation with your videos at api.video, feel free to reach out on our [community forum](https://community.api.video) or comment on the [GitHub repo](https://github.com/apivideo/videoModeration).  We'd love to see how you are using moderation to sort and categorise your videos, and the rubrics you utilise to decide what categories a video might fall into. If you haven't created your api.video account yet, you can do that in just a fewmomnets by follwoing [this link](https://dashboard.api.video/register). Happy building! 

Video moderation with machine learning

video content, machine learning, moderator, AI moderator

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api.video enables developers to build, scale and operate video in their own apps and platforms in minutes, with just a few lines of code. 
The service handles the end-to-end workflow, from video ingestion to worldwide video delivery. You can [test it for free](https://dashboard.api.video/register) right away and start building.

**August 2021 update**
Since this post was written, we've published a library to simplify JavaScript upload of videos [read the blog post to learn more](https://api.video/blog/tutorials/new-video-uploader-javascript-library/).

**17 February 2021 update**
The example code has been added to extract the filename, and then append it to the first upload. Without this step, your video title will be 'blob.'


You can view the API reference documentation for the file upload endpoint here: [Upload a video](https://docs.api.video/reference/api/Videos#upload-a-video)

***

As our presentations, PDFs, files, and videos get larger and larger, we are stretching remote servers’ ability to accept our files. With just a few lines of JavaScript, we can ensure that this error goes away, no matter what you are trying to upload. Keep reading to learn more.

The most common error with large uploads is the server response: **HTTP 413:  Request Entity too Large**. Since the server is configured only to accept files to a specific size, it will reject any file larger than that limit.
One possible resolution would be to edit your server settings to allow for larger uploads, but sometimes this is not possible for security or other reasons. (If the server limit gets raised to 2GB for videos, imagine the images that might get uploaded!)

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Further, if a large file fails during upload, you may have to start the upload all over again.  How many times have you gotten an “upload failed” at 95% complete?  Utterly frustrating!

## Segments/Chunks

When you watch a streaming video from [api.video](https://api.video), Netflix or YouTube, the large video files are broken into smaller segments for transmission.  Then [the player](https://api.video/player/) on your device reassembles the video segments to play back in the correct order. 
What if we could do the same with our large file uploads? Break the large file into smaller segments and upload each one separately? We can, and even better, we can do it in a way that is seamless to our users!

Baked into JavaScript are the File API and the Blob API, with full support across the browser landscape:

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This API lets us accept a large file from our customer, and use the browser locally to  break it up into smaller segments, with our customers being none the wiser!

Let’s walk through how you might use this to upload a large video to api.video.  
To follow along, the code is available on [Github](https://github.com/apivideo/blobUpload), so feel free to clone the repo and run it locally.

To build your own uploader like this, you’ll need a free [api.video account](https://dashboard.api.video). Use this to create a [delegated upload token](https://docs.api.video/reference/api/Upload-Tokens). It takes just 3 steps to create using CURL and a terminal window. 


> A delegated token is a public upload key, and anyone with this key can upload videos into your api.video account. We recommend that you place a [TTL (time to live) on your token](https://api.video/blog/product-updates/video-access-management-how-to-create-deliver-and-manage-private-videos-and-what/), so that it expires as soon as the video is uploaded.

Now that you're back, we'll begin the process of uploading large files.

### Markup

The HTML for our page is basic (we could pretty it up with CSS, but it's a demo 😛):

There is an input field for a video file, and then there are 2 divs where we will output information as the video uploads.

Next on the page is the ```<script>``` section - and here's where the heavy lifting will occur.  

We begin by creating some JavaScript variables:

* input: the file input interface specified in the HTML.
* url:  the delegated upload url to api.video.  The token in the code above (and on Github) points to a sandbox instance, so videos will be watermarked and removed automatically after 24-72 hours. If you've created a delegated token, replace the url parameter 'to1R5LOYV0091XN3GQva27OS' with your token. 
* chunkCounter: Number of chunks that will be created.
* chunkSize: each chunk will be 6,000,000 bytes - just above the 5 MB minimum. For production, we can increase this to 100MB or similar.
* videoId: the delegated upload will assign a videoId on the api.video service. This is used on subsequent uploads to identify the segments, ensuring that the video is identified properly for reassembly at the server.
* playerUrl: Upon successful upload, this will output the playback url for the api.video player.

Next, we create an EventListener on the input - when a file is added, split up the file and begin the upload process:

We name the file uploaded as 'file'. To determine the number of chunks to upload, we divide the file size by the chunk size. We round the number round up, as any 'remainder' less than 6M bytes will be the final chunk to be uploaded. This is then written onto the page for the user to see. (In a real product, your users probably do not care about this, but for a demo, it is fun to see).

cat that appears to be sliced into segments

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## Slicing up the file

The function createChunk slices up the file.

Next, we begin to break the file into chunks.  Since the file is zero indexed, you might think that the last byte of the chunk we create should be ```chunkSize -1```, and you would be correct. However, we do not subtract one from the chunkSize.

> the first byte that will *not* be included in the new Blob (i.e. the byte exactly at this index is not included).

So, we must use ```chunkSize```, as it will be the first **byte** NOT included in the new Blob.

In the createChunk function, we determine which chunk we are uploading by incrementing the chunkCounter, and again calculate the end of the chunk (recall that the last chunk will be smaller than chunkSize, and only needs to go to the end of the file).

In the first chunk uploaded, we append in the filename to name the file (if we omit this, the file will be named 'blob.'

### The actual slice command

The ```file.slice``` breaks up the video into a 'chunk' for upload. We've begun the process of cutting up the file!

We then create a form to upload the video segment to the API. After the first segment is uploaded, the API returns a videoId that must be included in subsequent segments (so that the backend knows which video to add the segment to). 

On the first upload, the videoId has length zero, so this is ignored.  We add the chunk to the form, and then call the uploadChunk function to send this file to api.video. On subsequent uploads, the form will have both the videoId and the video segment.

## Uploading the chunk

Let's walk through the uploadChunk function:

We kick off the upload by creating a ```XMLHttpRequest``` to handle the upload. We add a listener so we can track the upload progress.  

### adding a byterange header

When doing a partial upload, you need to tell the server which 'bit' of the file you are sending - we use the byterange header to do this.

We add a header to this request with the byterange of the chunk being uploaded.
>Note that in this case, the end of the byterange should be the last byte of the segment, so this value is one byte smaller than the slice command we used to create the chunk.

The header will look something like this: 

### Upload progress updates

While the video chunk is uploading, we can update the upload progress on the page, so our user knows that everything is working properly.  We created the progress listener at the beginning of the uploadChunk function. Now we can define what it does:

First, we do a little bit of math to compute the progress. For each chunk we can calculate the percentage uploaded (```percentComplete```). Again, a fun value for the demo, but not useful for real users.

What our users want is the ```totalPercentComplete```, a sum of the existing chunks uploaded, but the amount currently being uploaded.

For the sake of this demo, all of these values are written to the 'chunk-information' div on the page.

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### Chunk upload complete

Once a chunk is fully uploaded, we run the following code (in the onload event).

When the file segment is uploaded, the API returns a JSON response with the VideoId.  We add this to the videoId variable, so it can be included in subsequent uploads. 

To upload the next chunk, we increment the bytrange ```start``` variable by the chunkSize. If we have not reached the end of the file, we call the createChunk function with the videoId and the start. This will recursively upload each subsequent slice of the large file, continuing until we reach the end of the file.

### Upload complete

When ```start > file.size```, we know that the file has been completely uploaded to the server, and our work is complete! In this example, we know that the server can accept 5 MB files, so we break up the video into many smaller segments to fit uder the server size maximum.

When the last segment is uploaded, the api.video response contains the full video response (similar to the [get video endpoint](https://docs.api.video/reference/api/Videos#retrieve-a-video-object)). This response includes the player url that is used to watch the video. We add this value to the ```playerUrl``` variable, and add a link on the page so that the user can see their video. And with that, we've done it!

page showing upload complete - with link to see the completed video

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## Conclusion

In this post, we use a form to accept a large file from our user. To prevent any 413: file too large upload errors, we use the ```file.slice``` API in the users' browser to break up the file locally. We can subsequently upload each segment until the entire file has been completely uploaded to the server.  All of this is done without any work from the end user. No more "file too large" error messages, improving the customer experience by abstracting a complex problem with an invisible solution!

When building a video uploading infrastructure, it us great to know that browser APIs can make your job building upload tools easy and painless for your users.

Are you using the File and Blob APIs in your upload service? Let us know how!
If you'd like to try it out, your can create a [free account](https://dashboard.api.video/register) and use the Sandbox environment for your tests.  
If this has helped you, leave a comment in our [community forum](https://community.api.video).



Uploading large files with JavaScript

Uploading large files with JavaScript: File.slice() to the rescue!

Authenticate

At api.video, we offer a number of ways to upload video into your account. When you [create and upload a video](https://api.video/blog/tutorials/video-upload-tutorial/), the first step is to authenticate with your API, and generate an authentication token.  

For a static website, it may not be feasible to create a new token on each page load. For instances like these, we offer a delegated upload token.  This token works like a public key to your api.video account, allowing secure uploads without exposing your API key.

## Generating a Delegated Upload Token

The first step in creating a delegated upload is to generate your token.  First we authenticate with your private key :
>Note: I am using the sandbox API endpoint AND my sandbox API key in this example. To use production, point to https://ws.api.video, and use your production key.


This will return a JSON response with your authentication token:

We’ll use this token to request the delegated upload token:

The [delegated upload API](https://docs.api.video/reference/api/Upload-Tokens#generate-an-upload-token) will create a new token:

This will result in JSON response with the delegated access token:

```{"token":"to1R5LOYV0091XN3GQva27OS"}```



This token can now be used to upload videos into your api.video account. In the example below, I am uploading a video of clouds into my sandbox account.  


Go ahead and try this yourself!  Since the delegated upload is into my sandbox account, the video will be watermarked and deleted after 72 hours. Simply point the ```-F file@``` to a video on your local file system, and it will be uploaded into my sandbox.

You'll receive a response similar to this:


clouds.mp4

These urls can be shared for others to watch your video. On opening the player link, you might see a video like this: (Note the video link in the JSON above has expired and will throw a 404 error).

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## Expiring Delegated tokens

Delegated tokens act like a public API - anyone with this token and the api.video url could upload videos into your account. For this reason, you can [revoke](https://docs.api.video/reference/api/Upload-Tokens#delete-an-upload-token) delegated tokens so they can no longer be used.

## Delegated Token Time To Live

In addition to expiration, you can also set a 'time to live' (TTL) when creating a token. Once the token expires, itc an no longer accept uploads.  If you generate the token when the uplaod begins - you must ensure that at least one segment of the video is uploaded before the token expires, or the video will be rejected at the server.  You can split the video with the [JavaScript file API](https://api.video/blog/tutorials/uploading-large-files-with-javascript/).  Once the token expires, you'll have 24 hours to complete the rest of the upload (which should be PLENTY of time).

## HTML 
The [API docs](https://docs.api.video/reference/api/Videos#upload-a-video) have a thorough explanation on how to build a form and upload a video.

## Conclusion

Let us know how you are using delegated upload in your video service in our [community forum](https://community.api.video)


Delegated uploads: Make video uploads with a created token

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Delegated uploads: Make video uploads with a token you create

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Cédric Montet

Product updates

At api.video, our goal is for video to be easy and affordable for anyone to build, be it individuals or organizations. We understand that achieving this goal means reducing costs at some stage of the video streaming process. That’s why, we now offer adaptive bitrate encoding for your videos at zero cost. 

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Yes, you heard me right. 

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**Encoding videos will be absolutely free for all video qualities (1080p or 4K) at [api.video](https://api.video/) - no hidden calculations, footer notes or tricks.**

(Check out this [video](https://www.youtube.com/watch?v=ouosgLbW7Ts) for more details) 


## A step towards making video streaming affordable

Video encoding is crucial as it makes it possible for videos to be shared and played smoothly on the internet. It is why we are easily able to consume various types of video media, whether it’s a YouTube video, our favorite shows on Netflix, or even catching up with friends across the globe on Zoom. Video encoding basically makes the world go around in regards to anything related to video and video content.

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We at api.video recognize that. From [increasing the transcoding and playback speed ](https://api.video/blog/product-updates/2021-in-review/)to building [specialized hardware like ASICs](https://api.video/blog/video-trends/the-comeback-of-specialized-hardware/) cards to increase our transcoding capacities, we've been working to make video streaming easier for you every day. And after dedicating ample time to engaging with our customers and learning about their concerns, free encoding seemed to be the next step in this direction. 

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So, we worked tirelessly behind the scenes to give you what no one else will - free encoding for all video qualities, be it 360p or 4K. 

## How does free video encoding benefit you as a customer

The benefit is straightforward: you save a LOT of money!

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- With free video encoding, you will have one less thing to worry about. As you know, you pay for three things for streaming videos — encoding, hosting, and delivery – out of which encoding is the most expensive, especially if you have a lot of videos. Free video encoding will allow you to focus your attention and resources to what is most important.

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- As the video needs of your app users grow, you might need to scale up your encoding capacity. Free video encoding services will give you the power to scale without any additional costs, meaning your users can handle more videos without increasing your expenses.

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- You can [migrate from your previous provider to api.video](https://api.video/compare/azure-media-services/) without paying an ‘entry tax’ to us. With free video encoding, you no longer need to spend money on encoding videos with a new provider when you have already paid for encoding with an older one.

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Now with all the saved cost, you can allocate your resources and money to a place where it is actually important – building your product and delighting your users. 

## How to get started

To use the free video encoding benefit, getting started is easy. 

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- If you’re a new user, [sign-up](https://dashboard.api.video/register) for a free sandbox account and start uploading your videos
- If you are looking to migrate from another vendor, [import](https://import.api.video/) your videos from your current vendor
- If you have any questions, [talk to us](https://meetings.hubspot.com/diego398/cc-talk-to-us?__hstc=54467703.754940701eda85e3c99a902d8ab0b988.1687266643206.1694417789132.1694427271479.149&__hssc=54467703.3.1694427271479&__hsfp=3654297906) by booking a slot in our calendar

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To know more about our[ features](https://api.video/features/), [pricing](https://api.video/pricing/) or ask any other questions, [talk to our team ](https://meetings.hubspot.com/diego398/cc-talk-to-us?__hstc=54467703.754940701eda85e3c99a902d8ab0b988.1687266643206.1694162373815.1694167498585.144&__hssc=54467703.1.1694167498585&__hsfp=3654297906)and we will help you out. 

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Or, you can straightaway [sign-up](https://dashboard.api.video/register) for a free sandbox account and check our product out for yourself. 


Huge pricing update: Introducing video encoding at no cost

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api.video team

Video trends

Modern technology has been helpful for an increasingly fast and simple method of consuming various types of video media, whether it’s a YouTube video, our favorite shows on Netflix, or even catching up with friends across the globe on Zoom. Despite the relative ease of doing anything related to video, a few complex processes take place behind the scenes, away from public view. One such process is video encoding.

Video encoding basically makes the world go around in regards to anything related to video and video content. Thanks to the technologies behind the video encoding process, it has become much easier for people and companies to share video content on the internet. But what exactly is video encoding, and what happens during the process? We’ll take a closer look at this and much more in this handy guide to video encoding.

## What is video encoding?

In a nutshell, video encoding occurs when you take raw video footage, either from a digital camera or an analog film camera, and digitally compress it to make it a more manageable file size. Video encoding happens daily in both professional and personal capacities, and many people don’t realize it.

But, without encoding, services like Netflix and YouTube would not exist – or, at least, not in the same way that it does today. Without it, we couldn’t send videos of our beloved pets to our friends and family as easily as we can today.

## Why is video encoding used?

There are a variety of reasons why video encoding is used, but all factors point to the single function of video encoding – **compression**.

This is done to ensure optimal performance and compatibility with a selected suite of software and hardware, be it for DVD/Blu-ray playback, mobile viewing, online video streaming, or general video editing. A file's video and audio information undergo transformation and compression during the encoding process, all in accordance with the requirements of the selected encoding standard.

On a professional level, the ability to compress raw video into more manageable sizes makes it easier to transmit video content such as training, promotion, presentation, and so on over the internet. 

On a personal level, video encoding makes it much easier to send videos taken from your phone directly to friends and family. Most modern-day personal devices have built-in video encoding capabilities to allow for this to happen without a second thought.

To put things in perspective, uncompressed or unencoded video can have a data rate that’s as low as 3,000 Mbps and as high as 12,000 Mbps, depending on the length and quality of the video recorded. 

**Without encoding, it would take up to days for anyone to not just transmit video, but to be able to consume it, too.**

## Differences between video encoding and video transcoding

You need to be familiar with two more [essential terms related to video encoding](https://api.video/what-is/) – transcoding and codecs.

Transcoding relates to the conversion from one digital encoding format to another. It takes the already encoded video file, decompresses it, and then alters it before re-encoding or compressing it into a different codec. Codecs are what allow encoding to occur in the first place. They are software or hardware that gives us the ability to compress video to make them more easily stored and delivered tightly.

While you may hear “encoding” and “transcoding” used interchangeably, the two terms actually mean different things. Putting it succinctly, encoding is the initial process of compressing a video from a source – such as a camera – into a codec, while transcoding takes an already encoded video file to convert it into a different codec.

## Examples of video codecs

In order to understand video encoding, you must first be familiar with two interrelated terms: [video codecs and video formats](https://api.video/blog/product-updates/every-video-format-codec-and-container-explained/). These also tend to be used interchangeably, but much like the terms encoding and transcoding, they refer to two different things.

Video formats are the type of files in which the compressed video/audio is stored. Also known as a “container”, you can tell what a video format is by looking at the file’s extension — the three to four characters that sit after a period in a file name. Here are some of the most common video formats used today:

* MP4
* FLV
* MOV
* MKV
* LXF
* MXF
* AVI
* WebM
* QT

On the other hand, **the codec is short for “Coder / Decoder” (compression / decompression)** and relates to the video compression algorithms that allow encoding to happen. Over the years, codecs have gone through multiple iterations as it coincides with the increasing sophistication and rise in the quality of filmmaking. 

If you’re a content creator, you might be aware of some of the video codecs that exist today:

- **H.264 – also known as AVC, this is the most widely-used video codec available on every platform and mobile device.**
- H.265 – also known as HEVC, stands for High-Efficiency Video Coding, and it’s slowly overtaking H.264 as the standard. This codec can compress video much more efficiently than its predecessor.
- **VP9 – is an open-source, royalty-free codec developed by Google and is considered the direct competitor of H.265. The video compression codec VP9 was created to reduce file sizes, making streaming videos simpler.**
- **AV1 – is another royalty-free, open-source codec released in 2019 by the non-profit Alliance for Open Media. It was developed to become the successor to Google’s VP9 and contend with HEVC more directly.**

## How do different technologies handle video encoding

As you may have suspected, there are a majority of ways that different technologies handle the video encoding process. Let’s take a look at the most common technologies that use video and how video encoding works on each of them:

### Video editing software

When it comes to video content creation, a majority of people will use one of two video editing software — Adobe Premiere Pro or Apple’s Final Cut Pro. Both of these video editing software handle video encoding in very similar ways: when exporting the finished video.

Depending on which device the video content is recorded on, it will already be encoded and formatted according to the settings of the device. For those using iPhones recording 1080p video at 25 frames per second (FPS), for example, the iPhone will automatically encode the video using H.264 and contain it in a .mov file format. The video editing software will “open up” the container to get to the encoded, compressed video file within so the editing process can begin.

After editing the footage is complete, it will need to be exported, which typically concerns video encoding. The edited raw media taken from a recording device such as a camera will be encoded via the codec of the person’s choosing before being put into a .mp4 container once they’ve hit “export”. As we’ve mentioned before, the most common codec used in video content creation is H.264 for HD videos, with 4K videos using H.265. 

### Websites

Different websites utilize different techniques to handle video encoding – with a majority of the major sites like YouTube, Netflix, and so on using their own technologies to handle the process. A majority of social media websites allow you to upload video files directly onto the site to share, although most of them require the files to be saved in specific containers. 

These websites will take your video file and transcode it before using their own systems and serves to re-encode it to match multiple display resolutions. This process will happen regardless of your video file's original codec and settings. Different websites have different processes, but companies that don’t have the capacity to build their own video encoding systems can use a third-party application programming interface (or API) to handle this process. These APIs act as [dedicated video encoder](https://api.video/encoding/) that allows companies to add on-demand videos to their websites more efficiently.

Using [api.video](http://api.video) as an example, all of the videos submitted to the platform will be transformed into H.264 codec in an MP4 container. This may be done using transmuxing and/or transcoding.

We expect the video bitrate to be lower than 16 Mbps for up to 1080p resolution and 50 Mbps for up to 4K resolution. We accept any bitrates, however, we will scale them down to the limits mentioned because anything above is usually challenging for most viewers’ connection to watch.

### API

The ability to generate, store, and distribute video is made possible via a video API. [api.video](http://api.video)’s solution can be tailored to suit any specific need. 

The backend process of video on demand (VOD) is exceptionally complex, even if you’re a software programmer specializing in video. It is necessary to encode, package, and typically transcode the data before sending it to any of several possible destinations. All of these things, and more, may be done with minimal fuss using api.video.

Our API basically keeps the complexities of streaming media hidden from view, so you can rapidly develop apps with as much control as you need. This saves time and effort so developers can concentrate on making the product.

## Conclusion

As you can see through this video encoding guide, a lot of work goes into making the video you watch and the video you share possible. Without video encoding compressing raw video footage, it could take days to upload and transmit video content over the internet and perhaps even longer to be able to download and enjoy them. 

The best part about encoding at api.video is that is offered at ZERO cost. You will only be charged for delivery and hosting of your videos, and the encoding will be free. Now integrate your videos in minutes with api.video or [contact us](https://api.video/contact) for more information.

A Guide to Video Encoding & Video Codecs

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Erikka Innes

Encoding and transcoding are often used interchangeably but they have slightly different definitions. **Encoding is the initial process of converting raw video files into a specific digital format or codec.** It involves compressing and packaging the video data, often to reduce file size while maintaining acceptable quality. Encoding prepares the video for storage, transmission, or playback.

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**Transcoding, on the other hand, involves converting a video from one digital format or codec to another.** This is typically done to optimize the video for different devices or platforms, adapt to varying network conditions, or ensure compatibility with specific playback requirements. Transcoding occurs after encoding and often involves changing resolution, bitrates, or formats without altering the content itself.

## What goes on behind encoding a video?

It's easy to say that encoding is when you go from an _uncompressed source_ to a _compressed source_, but you may ask, what exactly do compressed and uncompressed sources mean?

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Uncompressed sources are video files that contain data that comes directly from a camera sensor vs. encoded video. In the digital space, it's SDI (Serial Digital Interface) or HDMI (High Definition Multimedia Interface). Both of them are popular standards. You'll find HDMI in places like your GoPro camera, or a mini camcorder. These days, you might also find HDMI in professional settings, but SDI is more common due to its reliability. Another example of an uncompressed source would be something analog, for instance, VHS tapes.

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A compressed sources, on the other hand, are video files that have undergone compression during the encoding process. Compression involves reducing the file size by removing unnecessary information or encoding the data in a more efficient format. This process results in a smaller file size, making it more manageable for storage, transmission, and playback.


## What are the types of transcoding methods?

There are three types of transcoding, depending on what you are trying to do to get a video to suit a particular browser, player, or platform:

* **Lossless-to-lossless** - This type of transcoding maintains the quality of your video across formats but allows you to take advantage of better hardware or compression algorithms. 
* **Lossless-to-lossy** - This type of transcoding will decrease your video in quality, but it will benefit you in ways such as – you will get a smaller and/or faster playing file, or a file that is compatible with the requirements of a particular platform, player, or browser.
* **Lossy-to-lossless** - Sometimes people think this means they are regaining quality. This isn't the case in this type of transcoding. If you transcode from a lossy format to a lossless format, all that it means is that you will not lose additional quality in the conversion process. You will not regain data you already lost through compression.

## Codecs for encoding and transcoding

For both encoding and transcoding, you will need to make use of codecs. A codec's job is to compress and decompress video files. It's right there in the name -  **co**mpress **dec**ompress or encode decode. So basically, codec is an algorithm that takes the video and shrinks it or changes it into a format that makes it compatible with the requirements of wherever you want the video to play. There are many codecs because there are many ways to compress a video or livestream for playback.



## What is transmuxing?

Now that you know the difference between encoding and transcoding, let's throw one more term in there - transmuxing. Transmuxing is when you change the container for a video instead of the format. It's a way of repackaging a video to make it compatible somewhere without changing what's in the file. 

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This process is faster than encoding or transcoding and takes less computing power because you only change the container, not the file. It's great when you need to quickly make something available, but the downside is that it doesn't handle things like making multiple video resolutions available. If you choose a container type that's incompatible with your video file, you may also cause compatibility issues, resulting in not being able to play the video.

## In closing

* **Encoding** - Encoding happens when you compress a raw data file from a video camera sensor or an analog source like a VHS tape. 
* **Transcoding** - Transcoding happens when you take an already compressed video file and change it to another format.
* **Transmuxing** - Transmuxing happens when you change the container of a video file but leave the video file untouched. 

We hope that explains the difference between encoding and transcoding clearly. 

_If you are looking for an affordable video API solution that does not burn a hole in your pocket for transcoding and encoding videos, check out our offerings on our [homepage](https://api.video/) or [sign-up for a free account here](https://dashboard.api.video/register)!_

Encoding and transcoding: what's the difference? 

Encoding and Transcoding - What's the Difference?

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Once I upload my video, how quickly will it be ready to be viewed?

Our reply to this question has always been “it's really fast.”  And it is really fast. But, the dev team gave me access to the backend database, and pointed me to a few tables that might be helpful (thanks to Thomas Persohn for your patient help!)

Thus armed, I waded into a sea of data to better understand how fast our videos are encoded at api.video

## Encoding

When a video is uploaded to api.video, it is re-encoded to a HLS stream, with several different video dimensions and bitrates.  This allows api.video to deliver properly sized video to all users - no matter the device or network connection. (Read our post on [Responsive video delivery](https://api.video/blog/video-trends/responsive-video-delivery/) to learn more.)

> Summary:  All videos are encoded at 240p, 360p, 480p, 720p, 1080p and 2160p. However, the videos are only encoded up to the initial size of the video (so if the original video is 720p, there will not be 1080p or 2160p versions available). 

**FYI:** For our customers, encoding is absolutely free – we offer adaptive bitrate encoding at zero cost. 

How long does this step take? If a customer uploads a video to our service - they want to know how efficient api.video creates the content for viewing.

### Data in the study

We are constantly making adjustments to our infrastructure to speed the encoding step of our video (and that is one reason this post is dated 2H 2020 - we expect to be faster in early 2021). The last major adjustment was made in mid October, so the data presented in this study from every video uploaded to the api.video service between 15 October and 2 December 2020.

### Parameters that affect encoding

There are 2 major parameters that affect the speed of encoding a new video stream: the output dimensions and the length (duration) of the video.  

#### Dimensions

The output video dimensions have a huge impact on video creation - a 3840 × 2160 (or 4K or 2160p) video has millions more pixels than a 320x240 (240p) video, so it takes a lot longer to create.  In general, the videos exit the encoding process from smallest to largest.

#### Duration

If a video is 30 frames per second, and is 60 seconds long, there are 1800 frames to be created.  If a 30 frame per second video is an hour long 3600s), there are 108,000 frames to be created, so the longer the video - the more time for creation.

## Analysis

To better understand the encoding time, I divided the data by the size of the output video size, and looked at 2 lengths of video  - those under 5 minutes, and videos that are 45-75 minutes long (we are often asked how long a 1 hour video takes to encode).

I calculated encoding time for each bucket of video using percentiles.  

> Quick review - percentiles break the data into 100 evenly sized segments, so the 25th percentile is when 25% of the videos have been encoded. The median is the 50th percentile, and is when 50% of videos have been encoded.  We report here to the 95th percentile for video encoding.

### Short videos - under 5 minutes long

Many of the videos uploaded to api.video are short - under 5 minutes long.  These videos are generally ready to play in a few seconds:



The above chart has a lot of data, the 25, 50, 75, 90 and 95th percentile encoding times for all 6 variants of the video that api.video creates.

The median video is ready to play (at 240p) in 5.6 seconds, but a 720p version will be ready in 15s after encoding begins, and the 4K version will be ready in 37 seconds.

95% of all videos (under 5 minutes) will be playable in 32.6s, and will have 1080p versions ready in just over 2 minutes (140s).

### Long videos - 45 - 75 minutes long

Our most frequently asked question on encoding is "how long for a 1 hour video to be encoded and ready?"  Here are the numbers:


Observant viewers will see there are no 4K videos in this report. At this time, there are very few 4K streams that reach the 1 hour timeframe, and the data was quite noisy.  Hopefully, more of our 'long uploads' will add 4K support, so we can add this metric in our next update.

The median hour long video (red line) will be ready to play (at 240p) in just over 9 minutes (555s), and a 720p version will be ready about 18 minutes after encoding begins (1099s).

95% of hour long videos will have a 240p version ready in 33 minutes, and 720p in 54 (3230s).  the 1080p version will be available for 95% of videos in 108 minutes,

## Conclusion

Short videos will be ready to play - even at high resolution - in under a minute: the median 4K encoding for a short video is 37s. 

Hour long videos take longer to encode, and the median 720p video will be ready in 18 minutes.  95% of 720p videos will be ready to play in 54 minutes.

This is really fast! You can have responsive streaming video ready for your customers in just a few minutes with a simple upload!

That said, our team is working hard in the background to improve our infrastructure to make our encoding speeds even faster! 

We're really excited to have hard numbers to share with you on our encoding speeds, and we cannot wait to re-benchmark this data in 2021 to see how much faster your videos will be encoded as we improve our service.

Do you have questions about api.video or video encoding? Reach out on our [community page](https://community.api.video).


How fast does api.video encode video? 2H 2020 data

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Huge pricing update! Introducing video encoding at zero cost

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Huge pricing update! Introducing video encoding at zero cost