vimeo

Video upload

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Arushi Gupta

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Video trends

_“Once a new technology rolls over you, if you're not part of the steamroller, you're part of the road.”_ - Stewart Brand, Writer

&nbsp;

Picture yourself in your office or home, daydreaming about your next thrilling vacation. Two options lie before you: flipping through a travel booklet, or immersing yourself in captivating videos showcasing breathtaking landscapes, mountains, oceans, delicious food, vibrant cultures, and more! If you had to choose, you will surely choose the second option, because let’s all agree, videos are far more realistic and give you the right information quickly. !

&nbsp;

But there are various formats to videos, and video-on-demand is one format in which videos are consumed today — thanks to video-on-demand (VOD) platforms like Netflix, Hulu, Hotstar, and more. 

&nbsp;

Now what are VOD platforms in the first place and how do they work? Let’s find out. 

## What is VOD?

Let’s first understand what is on-demand. On-demand is something that is desired for or wanted. Similarly, [video-on-demand ](https://api.video/what-is/vod-video-on-demand/)or VOD is any content format that gives the audience the power to watch it whenever, wherever, and however they want. It can be found anywhere on the internet for any purpose — be it entertainment, online learning, or e-commerce. Since VOD is streamed through the internet, there is no intervention of cable or satellite connections unlike traditional broadcasting methods. 

&nbsp;

And that is what makes VOD so popular and also, so convenient to use. 

## Benefits of VOD

Now that we know the meaning of VOD, let's look at some of its important benefits.

### VOD offers enhanced engagement

With VOD, you can create multiple videos for your products and services, providing proper descriptions of their features and functionalities. Attendees can watch, understand, and learn about your offerings through these on-demand streaming videos. Moreover, it is a common habit for people to watch various videos, read content, and then make product comparisons before making a decision. This provides you with an opportunity to capture the attention of a global audience for your products and services. 

&nbsp;

_Did you know?_

According to a 2022 study by [Wyzowld](https://www.wyzowl.com/video-marketing-statistics/), it was found that a massive 94% of marketers agree that video helped them understand a product or service better. It was also noted that 88% of people had been convinced to buy a product or service after watching the brand's video. Not to mention, the study also found that 96% of people watched an explainer video to know more about a product or service.



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### VOD is convenient for your business

Video on demand allows your audience or customers to watch content according to their will. There are no restrictions on place and time, and they can watch the video as they wish. The best part is that unlike most other general broadcasting services, VOD platforms’ availability is not restricted to certain geographical locations, so viewers from around the world can stream content. 

&nbsp;

_Did you know?_

[77% of Americans](https://www.statista.com/statistics/949965/pay-tv-streaming-satisfaction-rate/) report being happy with the convenience and flexibility of streaming video on demand, while [71% discarded cable TV](https://www.statista.com/statistics/319924/reasons-not-have-cable-usa/) as they can easily find the content they want online. 


### VOD tastes like freedom

VOD platforms offer the freedom to create content with complete control, free from advertisers' guidelines and restrictions. Producing videos-on-demand allows you to share your authentic self with the world. With just an idea, a camera, and an internet connection, you can craft the perfect video content for your product or service, ready to be showcased on your app or website.


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### VOD offers better opportunities at lesser cost

Video-on-demand proves more budget-friendly than hosting a virtual event or live streaming content. As opposed to livestreaming where you can be online for just a defined time — like a day or a week — video-on-demand has no restriction on the availability of your content and that allows people access to your content for a larger duration of time.


## What makes a great VOD platform

There are various aspects of a good VOD streaming platform. Let’s see what they are.

1. ### Customization with a white-label video player

The foremost focus of your product lies on branding. It is always great to have logos, images, and colors to maintain your brand's prominence by white-labeling your videos — and a good VOD platform should let you do that with ease. 

&nbsp;

At api.video, you get complete control to showcase your brand and [stand out with our customizable video streaming API](https://api.video/blog/product-updates/white-labeling-api-video/).

### 2. Content delivery network (CDN)

A content delivery network (CDN) is like the backbone of a VOD platform. A CDN plays a crucial role in delivering videos with minimal buffer time to your users across the globe, making sure they can enjoy smooth and speedy streaming.

### 3. Digital rights management

Digital Rights Management (DRM) is a tech tool that safeguards your content from piracy and unauthorized sharing. A good VOD platform usually has a DRM solution that lets you create videos and restrict their viewership to private or public. 

### 4. Multi-user access

A multi-user access feature provides custom access controls and user permissions within your business and for each target audience group. With the [help of built-in safeguards, a good VOD platform](https://api.video/blog/product-updates/workspace/) lets you share your workspaces with other members and collaborate on a project. 

## How is VOD different from live streaming?

The best way to describe the difference between VOD and live streaming is to understand real acting vs. reel acting. In theater, there are no second chances; what happens on stage is the final act. On the other hand, movies and series permit multiple takes, extensive editing, and color correction before producing the final video. Similarly, VOD empowers you to shoot, edit, and upload videos at your pace, granting you the freedom to pause, play, rewind and fast forward. 


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On the other hand, live streaming lacks such options since it takes place in real-time, untouched by editing. If you see the broader aspect, live streaming is a subset `of VOD as live videos can be watched on demand by the viewers at a later date. 

If you are wondering which one’s better, we would say both of them can be combined for a wholesome business package. While VOD offers convenience and accessibility, live streaming is more engaging for the user. 

## Is VOD the right option for you?

In a word, YES. There can be a lot to do in this era of online marketing, but video-on-demand can change your brand in a lot of ways. It is attractive because it grants you complete control over your videos without adhering to advertisers' guidelines. With the ability to shoot, edit, and upload at your own pace, VOD caters to diverse needs and audiences. Viewers can indulge in on-demand content, pause, play, and revisit their favorite moments at their convenience. 

&nbsp;

In a nutshell, VOD is an excellent way to connect with your audience and make a meaningful impact that can go a long way in your business’ growth but you need to evaluate your content goals, target audience, and desired level of control to be able to decide if it is the right option for your business. 

&nbsp;

_If you are looking for an API that lets you integrate videos seamlessly to your product, checkout [api.video](https://api.video/). With usage based pricing that does not burn a hole in your pocket, lightning fast encoding and a developer friendly API, you get everything you need at api.video to integrate VOD in your app or website._

&nbsp;

_[Sign-up ](https://dashboard.api.video/register)for a free sandbox account or [take a look at our features ](https://api.video/features/)now!_

What is VOD and how does it work?

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Artem Matinian

With the upcoming [retirement of Azure Media Services in mid-2024](https://learn.microsoft.com/en-us/azure/media-services/latest/azure-media-services-retirement), you may be wondering about what to do next. But worry not, because we  have a better and a far more affordable option for you! In this article, we will discuss seamless migration options from Azure to [api.video](https://api.video), give you reasons as to why you should be doing it, and also explain to you a simplified process to execute the migration. 

&nbsp;

Let’s get started. 

## Why should I migrate from Azure Media Services to api.video?

First things first, why should you think about migrating to api.video? What is the benefit? How is it better? 

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Well, let me answer all these questions with some solid facts.

### 1. api.video charges you zero cost for adaptive bitrate encoding

Let’s understand this with an example where we compare the encoding of 10,000 minutes at 4K resolution. 

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Azure takes **$600** to encode a video of 10,000 minutes at a 4K resolution as you can see in the image below (_source:_ [Azure Pricing Calculator](https://azure.microsoft.com/en-us/pricing/calculator/?service=media-services)).

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On the other hand, api.video takes ZERO cost to encode your videos. Yes, encoding is completely free with api.video, whether it is 10,000 minutes of video or 20,000; 480p resolution or 4K – it is absolutely free. 
 
 &nbsp;

_Check our pricing calculator to learn more about our [pricing](https://api.video/pricing/)._

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### 2. **Ingestion speed with [api.video](https://api.video) is way better than that with Azure.**

Let’s understand this by uploading and transcoding the same 18.7Mb file to Azure and api.video.

&nbsp;

When you transcode a video with Azure, the time it takes to do the job is 1 minutes and 13 seconds. Note that this is just time taken for transcoding, and I have not yet included the upload time here. 

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When you upload *and* transcode the same video with api.video, the time it takes to do the job is JUST 30 seconds. 

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### 3. **Ease of use of [api.video](https://api.video) surpasses that of Azure**

The process to upload, encode and transcode videos at Azure is complex and often tedious to execute. api.video, on the other hand, has built an interface and documentation that is easy to understand and quick to execute. To validate what we claim, do give the below mentioned links a read and know it for yourself. 🙂

<div id="migration-steps"/>

- **api.video:** [Upload and encode a video with api.video](https://docs.api.video/vod/upload-a-video-regular-upload)
- **Azure**: [Upload and encode a video with Azure Media Services](https://learn.microsoft.com/en-us/azure/media-services/latest/asset-upload-media-how-to)


&nbsp;

Sounds good? If yes, then let’s move to the actual migration steps.


## Migration steps to move from Azure to api.video

If you’ve made up your mind to make the shift, let’s understand the migration steps involved in it to move all your data from Azure Media Services to api.video. 

You can also migrate your videos directly through our [Import Tool](https://import.api.video/). 

&nbsp;

Here's a quick video explaining how to navigate through the Import Tool.

To start with the migration steps, let us look at a ready script in Node.js for you to follow. Before you start the script, make sure you clone the Azure Media Service to api.video migration [script] from (https://github.com/apivideo/azure-media-services-api-video-migration) Github. Once you do that, you can follow the guide below.

&nbsp;

Let’s understand what the script is doing.

&nbsp;

1. The script will check for all assets in your Azure Media Service Instance
2. It will get the Storage containers and list the blobs for these storage containers
3. Because of the way Azure is encoding their videos, the script will take the file with the highest resolution (in order not to duplicate the files)
4. Once it gets the list, it will create a video object with the name same as the blob and set the source to the Azure storage URL 
5. The file will then be uploaded and encoded with api.video

&nbsp;

You can also navigate to the api.video documentation to get more information about how each endpoint works. You can also check the [api.video api reference](https://docs.api.video/reference) or the [api.video guides](https://docs.api.video/) for more details.

&nbsp;

*Note that the script does not automatically delete the files from your Azure Storage or Azure Media Services, you will have to do that manually. If you wish to keep the sources on Azure Account Storage as backup, you are also welcome to do so.*

## Using the migration script

Here are the steps to use the script to migrate from Azure to api.video.

&nbsp;

1. First, import the modules that are needed, such as:
-    `@api.video/nodejs-client` - api.video node.js client, in order to create the video object, upload and encode the video
-    `@azure/identity` - access Azure services
-    `@azure/arm-mediaservices` - client to access Azure media services

&nbsp;

2. Next, pass in credentials that you get from Azure Media Services to the client and the [api.video](https://api.video) client

&nbsp;


3. Note that in the previous step, you will also need to update the `.env` file and grab the parameters from Azure. In order to do that, navigate to your Azure Media Service, and select the directory that you would like to migrate.

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4. Select API keys and copy over the parameters presented in the `.ENV` pane (you can select either **User Authentication** or ****Service principal authentication)****
    
 

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5. Next, to get access to the storage, navigate to the **Azure Storage Account** → **Shared access signature**:

Azure Media Services Shared Access Signature

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7. Make sure that you generate the Shared access signature and once you have the links, copy the Blob service SAS URL link to the `.env` file. The parameter you are looking for is `REMOTESTORAGEACCOUNTSAS`

Azure Media Services Blob Service SAS URL

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8. Add the api.video API key to the `.env` file by navigating to the api.video [dashboard](https://dashboard.api.video/project-settings/api-keys) and copy the API key value

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9. Now, run `npm install` in the terminal in order to install all of the node modules
10. Run `npm run build`
11. Run `npm run start` in order to start the migration script
12. Sit back and relax 🙂

## Conclusion

Well, that was it! Yes, it was that simple. 

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We hope that you choose wisely as you weigh the strengths of Azure and api.video. However, if you consider factors like scalability, customization options, pricing, and developer friendly API, api.video comes out to be a clear winner.

&nbsp;

If you still have any questions regarding the migration, [book a call with us](https://meetings.hubspot.com/apivideo/talk-to-us) — we are here to help you. Or [sign-up for a free sandbox account](https://dashboard.api.video/register) to test us out for free.

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So, are you ready to make your life easier with this painless migration process?

Migrate from Azure Media Services to api.video

How to migrate from Azure Media Services to api.video

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Analytics

Video Sessions (Analytics)

Normal

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Doug Twitter

Doug Sillars

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Tutorials

In the classic movie *Field of Dreams*, a mysterious voice whispers to Kevin Costner “if you build it, they will come.”  


<video autoplay muted loop>
		<source src="https://media.giphy.com/media/gZuxOq7zSL5DO/giphy.mp4">
</video><br/><br/>


His belief of this statement completely transformed his life and drove him to build a baseball stadium in the middle of a cornfield in Iowa… and they came…<br/>


<video autoplay muted loop>
		<source src="https://media.giphy.com/media/z3WMjg3hu3ce4/giphy.mp4">
</video><br/><br/>


## If you Stream it...

You've created some awesome videos, and uploaded them to api.video.  Great work - it looks awesome.  

Here is a video that we posted in our blog post announcing our new video player:

<iframe src="https://embed.api.video/vod/viTLpKB231ATWOWjkHgi9bf#autoplay#loop" frameborder="0" scrolling="no" allowfullscreen=""></iframe>
<br/><br/>

## ...Who will come?

Eventually you want to know, how many people came to see my video?  What can I learn about them? Enter api.video’s powerful [video analytics](https://docs.api.video/reference/api/Analytics) API. Using the API, we can transfer all of the view data form our videos into our favourite database, and learn about the viewers of our videos.

Let’s start with grabbing the viewing details for a video. In this case, I’ll use this Planets video that my daughter recorded for her class on astronomy:

<iframe src="https://embed.api.video/vod/vi16NDdXmGxk5QSggW54qw0v#autoplay#loop"  frameborder="0" scrolling="no" allowfullscreen=""></iframe>

The url for this video is: https://embed.api.video/vod/vi16NDdXmGxk5QSggW54qw0v

First I must [authenticate](https://api.video/blog/tutorials/authentication-tutorial/) myself at API.video, and I receive an access token that is good for an hour.

With the token, I can use the videoId to request all of the view information about my video with the [analytics API](https://docs.api.video/reference/api/Analytics).  The videoId for the video is the value at the end of the url (vi16NDdXmGxk5QSggW54qw0v):




In this case, I am requesting 25 results per page, and page one of the data.  That’s actually fine, since I know there are only 19 views on this video.  

The JSON result is pretty long, but here is what one entry looks like:


To me, this is super cool.  A person in Vietnam, using Firefox on Ubuntu, watched this video in June!  And there are 18 more of these to look at and to parse.

### Demo

We have created a demo that takes a videoId and counts the country, device and browser for each view, and places the results on a page. You can try it out at [whoswatched.a.video](https://whoswatched.a.video).

In order to look at the data, I am going to suck the JSON file into Couchbase - a database that uses JSON to create a "table" format similar to SQL. This will allow me to run queries on the JSON much like one would run a database query.

### Creating a database in Couchbase:

Download and install [Couchbase](https://www.couchbase.com/) on your computer.  When the install is complete, you’ll have an admin dashboard open in your browser (on my Mac it is running on localhost:8901).  We’d like to add in our api.video JSON file into a data bucket.  To create the bucket - navigate to the Bucket section and click the "ADD BUCKET" button. I have the memory set at the minimum 100 MB (there are all of 19 entries after all!).


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Great - now we have our data bucket added.  Now we’ll want to import the JSON from api.video, so we can do our analysis.  This is done in the terminal using the ```cbimport``` command:

My username is Administrator with password 123456 (Google Chrome is mighty peeved about my use of an insecure password on localhost), adding a json file into the videoStats bucket.  Each video view has a sessionId, so I use that as a unique key.

I get a success message in my terminal, and looking back at the Couchbase admin panel under Documents, I can see each entry from the JSON has been added:


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Now we must index the data. In the Query Editor, enter:

Now we can do data analysis of our video views. For example, to understand the number of views per country:

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That’s pretty cool!  


## Live Stream Analytics

We can do exactly the same thing with live stream analytics. Note: these only show “views” of a live stream.  If you run a live stream and no one watches - there will be no analytics reported.  Further - if your live streams are saved as video on demand (VOD) for later use - any VOD analytics are saved with the newly created VOD video, and will have no connection to the original live stream.

Let’s look at a live stream that has a number of views.  On the [api.video](https://api.video) homepage, we have a demo that allows you to livestream right from the homepage.  The live streamer then “watches” their stream in the browser at the same time that they are broadcasting - guaranteeing that every live stream gets at least one viewer (in the same location as the live streamer!)

The API call is similar to that of the VOD request, I just use the 'live-streams' endpoint as opposed to the 'videos' endpoint I used above.

### GeoLocation  


The live stream demo is hosted in North America and in Europe, and we use geolocation to direct our users to the correct server. Let’s see how well our load balancers are doing. Traffic directed to North America is on the left, and European traffic is on the right:


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It looks like our load balancing is doing a bang up job. :)


Combining the two sets of data (I just created a new bucket with all the data added) allows us to see that api.video’s home page has been live streaming all around the world:


Couchbase results in Google Sheets to create this chart.

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### Devices & browsers

Most of the streams are from desktop machines. All 28 phone instances correspond directly to 28 Android devices.(This is due to a limitation in Safari that prevents the demo from working on any iOS device.)  Of our Android users, we see 25 using Chrome, 1 Samsung Internet, 1 Opera (and one hidden variant.)

Desktop users are mostly Windows and Mac (and pretty evenly distributed), but also a good number on Linux/Ubuntu).


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## Conclusion


The video analytics offered by api.video allow you to better understand who is consuming your content. In this post, we've walked through the steps to extract the data using the Analytics API in JSON format, and then ingested the data into a database for fast and easy queries.  

We used this data to see how the live stream demo on the api.video home page was used in July 2020.

How are you using api.video's analytics? Drop us a note in the [community forum](https://community.api.video) with your use cases!

Analytics with api.video

JavaScript

NodeJS

A chyron is the text that appears at the bottom of the TV on news channels telling viewers the topic being discussed.  You might want to add a chyron to your live stream or to your video on demand (VOD).  In this post, we'll walk through the steps to add a chyron to your video.





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## Chyron a video

The application (which will soon be published at chyron.a.video) extends https://record.a.video. You can read about how we built record.a.video in a [series of blog posts](https://api.video/blog/tutorials/grabbing-camera-streams-with-mediadevices-getusermedia/).  This application allows you to share your camera and your desktop, and creates a live stream or VOD recording of what you have shared.

We'll extend this by adding an icon to the left of the chyron (in the same approach as [watermark.a.video](https://api.video/blog/tutorials/watermarking-your-videos/). We'll then add a stripe over the video that the text will appear on.

## The code

The HTML adds 2 fields to the form: one for the logo url, and the 2nd for the text to be placed in the chyron. Along with some CSS, we style the form:

```
    <div class="settingHeadings">
                Chyron<br/>
            </div>
            <div id="watermark">
                <label for="watermark-url">image for chyron</label> 
                <input type="text" id="watermark-url" value="http://localhost:3037/API.PNG"><br/>
                <label for="chyron-text">text for chyron</label> 
                <input type="text" id="chyron-text" value="Your text here.">
            </div>
```

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### Starting the playback

When the stream is started, we add a few features to the canvas:

```
//chyron
        canvas.drawImage(watermarkImg, watermark_x, watermark_y);
        canvas.fillStyle='#fa4b2a'; 
        canvas.fillRect(chyron_x,chyron_y, chyron_length, chyron_height);
        canvas.fillStyle='#ffffff'; 
        canvas.font = "60px Arial";
        ctx.fillText(document.getElementById("chyron-text").value, chyronText_x, chyronText_y);
```

First we draw the icon (with variable name watermark) with the x,y positions hardcoded inthe JS.  We assume the size of the icon is 100x100.

Next we add a rectangle with fill color #fa4b2a (orange) that appears across the bottom of the screen.  

Then we change the fill color to white (#ffffff), and add the text from the form field.

These canvas events are all within a loop that fires every 20ms, or about 50 frames per second.  Since our video is capturing at a lower framerate than that, it will appear to update instantaneously.

## See it in action


In the video above, as the text on the browser window changes, so does the text in the chyron.  With 2 displays, you can now produce a live stream or VOD video with a chyron bar at the bottom of the screen.

## Try it yourself

The code for chyron a video is on [Github](https://github.com/dougsillars/chyronavideo). It's super easy to launch locally with NodeJS, and by changing the variables for the canvas colour and fill, you can easily adjust the font and look of the chyron to fit your requirements.

In this post, we've gone through the steps taken to convert record.a.video into a video with a chyron.  You may have even learned what a chyron is :).

Let us know what you think, and how you've changed the app to customize your chyron in you VOD or livestream in our [developer community](https://community.api.video)

Adding a chyron to your video: Look like the news!

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api.video

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Product updates

Did you know that once you become an [api.video](https://api.video) customer, you have the power to activate even more features?

&nbsp;

Yes, additional features we call ‘Add-Ons’.

&nbsp;

As it must be clear by the word itself, the Add-Ons offer you a wide set of ‘additional’ services that you can purchase to unlock the full potential of your video experience with api.video. The Add-Ons are primarily useful for teams looking to grow and scale faster.

&nbsp;

There are four Add-Ons that you get when you become a customer of api.video:

&nbsp;

- Projects
- Members
- Custom Domains
- Analytics

&nbsp;

Let’s look at them one by one.


## Projects

A ‘Project’ is a container within your workspace that allows you to isolate a set of videos, live streams, and player themes. Each Project holds its own API keys for authentication.

&nbsp;

Creating different Projects is especially helpful if you want to create a clear separation between different customers, companies, or even working environments. The way you choose to organize your videos, streams, and players with Projects will be completely at your disposal.


## Members

A ‘Member’ is any person who can access the api.video service. So, you can make others a member of your project and grant them the ability to access and contribute towards effective collaboration and communication.

&nbsp;

A ‘Member’ is any person who can access the api.video service. So, you can make others a member of your project and grant them the ability to access and contribute towards effective collaboration and communication.

&nbsp;

There are two types of members you can add to your projects:**

&nbsp;

**Owner**: One owner can be added to a single workspace and represents the person who owns the account. He/she can view the account's billing details, change the payment method, and is the only one that can transfer ownership.

&nbsp;

**Collaborators**: It is possible to add as many collaborators as you want per account, to a project. You can invite and work together with collaborators in the common workspace.


## Custom Domains

‘Custom Domains’ is a feature that lets you stream videos or live streams from your own domain (such as videos.yourdomain.com/ or livestreams.yourdomain.com/).

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It lets you brand your video URL, comply with network security and increase your SEO ranking.


## Analytics (Limited offer)

Analytics is an Add-On that allows you to see how your videos and streams are performing. It shows you the number of plays, how your videos perform in each country, device, and many other things through your dashboard.

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You can use this Add-On for free as a part of our free trial ending on July 28, 2023. 

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_Do [check it out ](https://dashboard.api.video/register)and use it to your advantage for these bonus days!_


## How are the Add-Ons helpful

Add-Ons can prove really helpful if you wish to optimize the complete potential of api.video. Here are some of the things you can achieve with Add-Ons:

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- Maximise your productivity by managing your customers via dedicated customer video libraries, and inviting collaborator members.
- Save on monthly fees by managing multiple production environments and accesses under a single workspace.
- Maximise your efficiency by facilitating coordination between your creators, editors, reviewers, and publishers, and encourage your customers’ firewall management with Custom Domains.
- Save the extra investment on Video Asset Management (VAM), Production Asset Management (PAM), and other Digital Asset Management (DAM) tools.
- Optimise your revenue by looking at the usage per project and maximizing the profit with your customers using Analytics.

## How to use Add-Ons for your business

Let me share examples of how Add-Ons can be used in three different industries. 

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**E-Learning:** If you are an e-learning platform or website, Projects can be useful to classify content and streams based on classrooms, teachers, or mandatory and pre-requisite learnings. Teachers or tutors of the platform can be added as Collaborators so that they can upload tutorials, lessons or conduct a class via live- streaming. Additionally, Custom Domains can help you manage multiple e-schools and their specific firewall requirements. 

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**Social Media**: If you are a social media platform, Projects can be helpful to classify content and streams based on brands and targeted destinations. Collaborators, who are often content creators, can easily upload videos via their dashboard, allowing non-technical people to assist in the process. Lastly, Custom Domains can be used to protect and maintain the brand identity through personalized domains.

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**Marketplace:** If you are a marketplace, you can use Projects to monetize your services based on your end customers' usage and maximize your profits. Influencers and content creators can be added as Members to your workspace and Custom Domains can be used to retain and establish the brand identity.

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To conclude, Add-Ons will be immensely helpful if you want to extract the maximum output from api.video tools. Use the Add-Ons to facilitate important business decisions, analyse your videos’ performance and collaborate for your ongoing projects.  

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_To try out Add-Ons before subscribing, [sign-up](https://dashboard.api.video/register) for a free via Sandbox account now!_

Unlock the complete potential of api.video using Add-Ons

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Jean-Baptiste

Have you ever wondered how a color can be represented in a video and how it can be reproduced on a wide variety of screens with almost the same rendering? Part of the answer lies in the concept of color space.

This first article will discuss the color theory and define the key elements necessary to build a color space for digital videos. A second part will follow and will focus on the most commonly known color spaces in the video/TV domain and how to analyze them using `ffmpeg`. So, let’s jump into the theory and talk about our eye capacities!

## A bit of biology
We cannot talk about colors without talking about our eye. As you will see, it is important to understand its capacities and weaknesses to efficiently represent colors in digital videos.

First, sorry to tell you that but colors do not exist in reality. Our eye can detect a subset of electromagnetic wavelengths from 380 to 700 nanometers; this is called the visible spectrum, and colors are an interpretation of our brain based on the wavelengths received by our eye. And it could act strangely. For example, the same exact color can be produced using different combination of wavelengths; this phenomenon is called `metamerism`. A simple example is the yellow color, it could be a single wavelength at 580 nanometers, but it can also be a mix of red and green wavelengths. Also almost all colors can be reproduced using a single wavelength except the magenta which is a mix of purple and red wavelengths.

To understand that behavior, we need to dig more into how our eye works. At its core, it is composed of `rod cells` and `cone cells`. `rod cells` can detect light brightness, they are mainly efficient in dark environments and are almost entirely responsible for our night vision. `cone cells`, on the other hand, can detect colors mainly in medium and high light brightness; they are of three types, `S-cone`, `M-cone`, and `L-cone`. Each `cone cell` has a specific response to wavelengths, and their peaks of sensitivity are respectively Blue (`S-Cone`), Green (`M-cone`), and Red (`L-cone`). They also have different sensitivity to brightness; concretely, the eye can detect more nuances of greens than it can for red ones or blue ones. To help you visualize how it works, here is a normalized view of the spectral sensitivity of each `cone cell` (keep in mind this is a normalized view, in fact, the peaks for each `cone cell` are not at the same level):

From there, we can explain the `metamerism` phenomena. The principle is to find different mix of wavelengths that produces the same response on `S-cone`, `M-cone`, and `L-cone` to get the same color. This is because `cone cells` generate an average response in the face of several wavelengths; therefore, you can get a yellow from a single wavelength but also by combining green and red, with an average response “in-between” on yellow. We can also explain why the magenta has no corresponding single wavelength. This is because it is a mix of extreme visible colors in the visible spectrum (purple and red), and no “in-between” single wavelength can produce the same response. Therefore our brain creates a new one to fill that gap.

That is a lot of concepts to digest, I know, but to oversimplify, what you can remember is that:

1. color is a brain interpretation of wavelengths received by our eye
2. our eye has a high sensitivity to brightness
3. our eye can detect colors with `cone cells` with a peak of sensitivity on Red, Green, and Blue
4. the same color could be produced using different combinations of wavelengths

Don’t worry; we will dig into the important concepts that are used to represent colors in the following sections.

### How bright is it?

Each visible color is perceived by our eye with a different brightness and this not only depends on the light intensity (photons). Take these blue and green images:

You perceive the green one as brighter, don’t you? This is because our eye is more sensitive to the Green color than it is to Blue. Remember what we said about `S-cone`, `M-cone`, and `L-cone`? They don’t have the same sensitivity regarding brightness. That perceived brightness of a color is called `luminance`, and it depends on the light intensity, of course, but also on the color itself, as we have just seen.

The `luminance` is measured in candela per square meter, also called `nits`. To give you an idea of the scale, a typical scene at sunrise is ~25 nits. The sun's disk at noon is ~1.6 billion nits! But don’t look at it! You risk retinal damages at ~100 million nits 😎.

### Are you dazzled?

Our eye can adapt to very wide `luminance` conditions. It can detect objects in very bright environments under sunlight but also in very dark environments during the night. The ratio between the highest `luminance` and lowest one can be as high as 15 000 000:1, but this is only achievable thanks to mechanical and chemical adaptions in our eye.

At a fixed configuration in a scene, our eye can distinguish `luminance` ratio from ~1 000:1 to ~15 000:1 depending on people and light conditions. When the ratio is higher, we start to lose information, and some colors disappear. This ratio between the highest and lowest luminance in a scene is called the `dynamic range`, and this is an important part to look at when trying to reproduce images that “feel“ more natural.

### What can we perceive?

To define a model to represent colors, we need to know what colors are perceived by our eye.

The *"Commission Internationale de l'éclairage"* (CIE) studied human color vision and worked on a model to represent it. In 1931, they created the `CIE XYZ` color space to represent all visible colors. Hum, it is named a color space but keeps in mind this is not the same concept as the one used for digital videos. The Y component was deliberately designed to represent `luminance`. But for X and Z… it is a bit abstract. We need to understand how this space was built to understand them.

Before 1931, the CIE conducted several experiments with humans to match different source colors with a single wavelength using a combination of red, green, and blue lights. The aim was to leverage how `cone cells` work with the metamerism phenomena. But... it didn’t work for all colors. In fact, it was sometimes necessary to add some of the 3 red, green, and blue colors to the source light instead of mixing them to get a match. This resulted in the concept of negative colors 😯. Using these experiments, the CIE was able to define a Red, Green, and Blue color space (`CIE RGB`) where all visible colors were represented but using negative values, which was not very convenient:

To overcome this problem, the CIE worked on a mathematical transformation to get a color space only using positive values and `luminance`. We won’t enter the mathematical details here, but this resulted in the creation of the `CIE XYZ` color space.  This is an abstract coordinate system but with the advantage of expressing any visible color with only positive values:

Well, you are lost? This is not over yet; another used system to represent visible colors is the `CIE xyY`. This is an equivalent color space to the `CIE XYZ` where (x, y) are normalized values independent from the `luminance` Y. There are called the `chrominance`. This color space is particularly useful as it allows to build a flat representation of colors at a given `luminance` which is called a `chromaticity diagram`:

Well, that diagram does not really represent all possible visible colors. First, you watch it through your screen, which cannot reproduce all colors. But more importantly, it is a flat view that could be built for each `luminance` value. A complete representation would require a 3 dimensions graph, but this one is more convenient for talking about color spaces, and we will use it in the second part for video color spaces.

Ok, just stop for a moment; there were a lot of things discussed, but what you should remember is:

1. the red, green, and blue lights cannot be used to reproduce all visible colors
2. the CIE built an abstract system to represent any visible color called `CIE XYZ`, where `Y` represents the `luminance`
3. the `CIE xyY` is an equivalent system to the `CIE XYZ` where (x, y) coordinates are independent of the `luminance` (`Y`) and are called `chrominance`
4. we have a convenient way to represent visible colors using the `chromaticity diagram`, even if it is not exhaustive
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## How does this map to a video color space?

Now that we have the theory behind color, let’s deep dive into how to represent it for digital videos.

### First, what colors do we target?

It is not practical in digital video to treat colors using all possible wavelengths. In the field, capturing devices rely on red, green, and blue cells to acquire color, and in the same way, screens re-produce them by combining red, green, and blue lights. Therefore a digital color is really tight to the RGB model, like in the CIE experiments. So, the first thing to define in a video color space is the reference red, green and blue colors to use in the `CIE XYZ` color space. These are called `primary colors`. All reachable colors in the video color space are contained within the triangle they formed in the `chromaticity diagram`. This also means that colors outside this triangle are not reproducible like we learned before! Here is the representation of the color space built by the CIE based on Red, Green, and Blue colors, e.g., `CIE RGB`. It is a really wide space, but as you can see, it does not cover all visible colors.

A color space also defines what is called the `white point`. Wait, what? Does that mean that we can choose what is white? Yes and no; when we talk about the `white point` we talk about a white object (in the common sense of white) observed using an ambient light. Depending on the ambient light, the object’s perceived color can vary. In digital video, the `white point` is often defined at a color temperature of 6500K. We won’t enter into the theory around why it is expressed using a temperature in Kelvin, but just remember that this value is, in fact, a white observed through the sun's daylight.

### Do we really need them all?

Human eye sensitivity to contrast (`luminance` difference) is not linear. It is more sensitive to darker tones than it is to brighter ones. In digital videos, colors are represented using discrete values; consequently, it is impossible to represent them all. It would be a waste of data to encode things our eye is not good at distinguishing. To solve that, a color space defines a mathematical function to distribute colors more efficiently, knowing our eye capacities. This formula is called the `transfer function`. The `luminance` corrected by this transfer function is called the `luma`.

A pretty common transfer function you probably heard of if you played with your camera or TV settings is the `gamma` correction. This is a power-law expression for which the exponent (the power) is named `gamma` hence its name. A `gamma` of 1/2.2, represented here, emulates more or less the response of our eye to contrast:

As you see in the produced grayscale, thanks to the transfer function, we use more dark colors than bright ones with `luma` compared to the linear `luminance` distribution just below; this is exactly the purpose when using a `transfer function`.

Another part defined by a `transfer function` in video color space is the `luminance` range in `nits` at which it operates and, so indirectly, the reachable `dynamic range`. Remember what we said about the sun disk at noon? When you watch it in a video, hopefully, it does not burn your eye. This is precisely because of the chosen range; there is no need to be able to encode such a high `luminance` unless we want to injure people. And in any case, the screen you are using cannot reproduce it!

So all of this is great, but now, if we step back, the fundamental role achieved by a `transfer function` for digital video is compression! Indeed, if we have a really good way to choose colors that matter to our eye, it means we can use less of them without losing much visual quality.

### Let’s focus on luminance

We discussed `luminance` and `luma`, but why does it matter? Can’t we use red, green, and blue instead? Well, we could, but it turns out this is not the most efficient way to represent colors in digital videos. Compression is really valuable in digital video; we already talked about the `transfer function`, but what if we can go deeper?

Again, we need to talk about our eye for that; if you remember, it is very good at distinguishing `luminance` but slightly less at distinguishing colors. To convince you about that, here is a simple optical illusion:

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Here triangles have the same color; can’t you see that? If you want to be convinced, click on the image.

As disturbing as it is, we can use this to our advantage. For that, we need to represent a color by decomposing its pure `luminance` from its color contribution (`chrominance`). We already talked about such a thing in a previous section, and it is the `CIE xyY`. Indeed, a variation named `Y'CbCr` is used because it is more convenient for computers. Here `Y'` stands for the `luma` (`luminance` corrected by the `transfer function`), `Cb` for the blue contribution, and `Cr` for the red contribution. To help you visualize how it works, here is a plane distribution of colors at a fixed `luma` (`Y’`):

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If you look closely, this linear representation is, in fact, a stretched view of the `primary colors` triangle in the `chromaticity diagram`.

Now, with this system to represent a color, we can compress the color information even further. As we said, our eye is not good a distinguishing the `chrominance` compared to the `luminance`, so we can keep the `luminance` data for all pixels and use fewer `chrominance` values; why not take 1 out of 2 pixels? This is exactly what is done in digital video, and this process is known as `chroma sub-sampling`. Honestly, it is a bit more complex than just taking 1 pixel out of 2 for `chrominance`. A common compression is 4:2:0, meaning you take 4 pixels of `luminance` and 2 pixels of each `chrominance` on the first line, then 4 pixels of `luminance` and 0 pixels of each `chrominance` in the second line, resulting in an overall compression of 50%! It exists other compression schemes like 4:2:2, 4:1:1, 3:1:1… you get the idea it tells you how many `chrominance` pixels you take compared to `luminance` ones, and based on the scheme, you get a more or less compression ratio:

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Here is an image with different `chroma sub-sampling` schemes, it gives you an idea of the impact on the quality:

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Something is still missing here; how do we convert an `R'G'B'` color to `Y'CbCr`? There is no absolute formula to do this transformation, and that’s something a video color space must define.

Here is an example of the `luma` and `chrominance` calculation given its Red, Green, and Blue parts:

We can immediately see from this formula that the Green part contributes more to the `luma` than the Red and, finally, the Blue. Of course, this is because of our eye, if you remember what we said about our `cone cells`. All these coefficients (also for `Cb` and `Cr`) need to be defined by a video color space to ensure the conversion of a `Y'CbCr` color into its `R'G'B'` equivalent.

Now, a bit of history, you will often see `YUV` used interchangeably with `YCbCr`. In fact, `YUV` terms come from analog TVs, and `YCbCr` is its digital equivalent. As we have seen, the `Y` part is the `luminance`, but it is no more, no less than the black and white part of the color. It was used at first on black and white screens to convey images. When color TVs arrived, there was a choice to make whether to switch on a new color system like `RGB` or keep the black and white signal `Y` and add to it the color contribution or chrominance `U` and `V`. Of course; the second option was chosen to avoid a big breaking change between color and black and white TVs. This also explains why this color system is so widely used in video.

### How many colors can we use?

Because colors are represented in a numeric format, we need to decide the number of bits that will be used to store them. Whether you use `RGB` or `YCbCr` you will need to encode three channels to encode a single color. The number of bits used to encode a single channel is called `color depth`. This number is directly linked to the range of colors it can represent. In the video domain, common values are:

- 8 bits: ~16 million colors
- 10 bits: ~1 billion colors
- 12 bits: ~69 billion colors

Remember that even if it seems huge, our eye can see much more colors than that.

So, you may wonder why don’t we use a bigger `color depth` ? This is because your video size would dramatically increase. Taking a single image in a 4K video requires encoding ~8 million colors, one for each pixel, representing ~285Mb using 12 bits `color depth`. And we only talk about a single image. Of course, they are ways to compress that, but this is just to give you an idea of how big it can be, even for a computer.

In that case, why don’t we use a smaller one? Like every time with computers, this is a trade-off. If we use a smaller `color depth` we may not be able to produce enough color shades. When looking at a gradient image, the transition between colors may not be smooth, and you will start to see some bands appearing. This effect is known as `color banding`, and of course, we want to avoid that. Here is an example of color banding on the left compared to a smooth color gradient on the right:

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### Don’t forget analog TVs

We cannot talk about digital videos without talking about analog ones. Due to technical constraints, the analog video signal was not only conveying information about colors; there was also non-visual data to sync new lines on the screen, for example. Also, due to its analog transport, colors were not always accurate. Because of that, when digital videos arrived, it was decided to reserve some values about the color data to encode non-visual information or conversion problems. This resulted in the creation of the color `limited range` or `tv` range.

Concretely, not all possible values were used to encode the color information. At that moment, colors were encoded using values from 0 to 255 (in 8 bits). It was decided to only use values from 16 to 235 for `luma` and 16 to 240 for `chrominance`. For the `luma` value, 16 is full black, and 235 is full white. That concretely means that we have fewer possible values to express colors. And this is always used for retro-compatibility in current digital videos, even for higher `color depth`. This is a specificity for videos and TV monitors; computers, on the other hand, use what is called a `full range` or `pc` range to express colors. It means all values from 0 to 255 are used to encode the color. 

The `color range` information, either `limited` or `full` is therefore critical in a video color space to be able to reproduce colors accurately.

## Conclusion

Great, you stayed focused until here! Here is a quick recap of what we have learned so far.

In order to build a color space for digital videos, we need to define:

1. Where are the `primary colors` and the `white point` to define what visible colors will be represented
2. What is the `transfer function` and its range in `nits` to use colors that matter to our eye in the best efficient way
3. What coefficients are used to convert `YCbCr` color into classic `RGB` and vice-versa?
4. What is the `color depth` used to know how many colors can be represented
5. Whether we use `limited range` (`tv`) or `full range` (`pc`) to encode color information

Is it all? Well, it would be too easy; a video color space also defines frame per second, resolution… and many other details. Here we only focus on generic concepts that define colors by abstracting video technical constraints.

If you want to learn more, stay tuned; in the next article, we will discuss standard video color spaces and how to identify them using `ffmpeg`.

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Emilien Gobillot

**The comeback of specialized hardware and its impact on the video industry**

Over the past few decades, the trend in computing has been toward general-purpose computing systems that can run a wide variety of applications. This has led to the widespread use of [CPUs and GPUs](https://www.intel.com/content/www/us/en/products/docs/processors/cpu-vs-gpu.html). However, there has been a recent resurgence of interest in specialized hardware, which is designed specifically to perform one or a few tasks extremely efficiently. In this blog post, we will explore the reasons behind this trend and some of the critical examples of specialized hardware being used in the video industry today.

**Why is specialized hardware making a comeback in computing?**

Specialized hardware in the computing industry refers to computer components or devices designed for a specific purpose, such as application-specific integrated circuits (ASICs)  or systems-on-a-chip (SoCs) . These hardware components are optimized for their specific task, resulting in faster and more efficient performance than general-purpose hardware.

Several factors have contributed to the recent resurgence of specialized hardware in computer science:

* **High efficiency : Why we want it quick and ressource-efficient**

One key driver is the increasing demand for high-performance solutions. Artificial intelligence, machine learning and video processing are domains where CPUs are far from being the right solution. Regarding AI workloads, training, and running models are more efficient on GPU and even better on specialized hardware like Tensor processing units (TPUs). Google has been the first to design TPUs. These ASICs (Application Specific Integrated Circuits) outperform the CPUs.

Why are CPUs outperformed ? In early 2023 commodity servers come with 1 or 2 sockets and maximum 96 cores per socket for the x86 microarchitecture (128 for ARM). On the other hand, a server with a mid range CPUs can receive several ASICs/TPUs on the PCIe ports. Those boosted systems outperforms by a lot the CPUs based system. The TPUv4, in terms of Tflops is 2000x faster than a laptop according to Google.

The benefits of a specialized hardware is that it provides better performance, higher density and a lower energy consumption than the classic CPUs.

In most cases, TPU/ASIC > GPU > CPU on the 3 criteria : performance, density, and energy savings.

* **Total Cost of Ownership (TCO): Why It's Worth the cost**

Specialized hardware exhibits smaller size, higer speed, and greater electrical efficiency. As a result the density increases and the number of computing units decreases. At the end, the TCO is improved. After the switch to ASICs based VCUs (Video coding unit), Youtube is [reported](https://www.semianalysis.com/p/google-new-custom-silicon-replaces) to have saved millions of CPUs ! At this scale you also save data centers 💰

**How specialized hardware is changing the game in the video industry**

In the video industry, one of the most computationally intensive processes is video transcoding, especially for high-quality videos. Transcoding decodes a video from one codec and encodes it to another, such as from H.264 to H.265. This process can also involve changing other video parameters like resolution, bit rate, frame rate, etc. This has led to a need for fast and efficient video transcoding solutions without sacrificing the video quality output.

Transcoding is a service api.video provides in addition to hosting and delivery. Via an API call, a video is uploaded, and then the URL of the video is returned immediately. Note that high resolution and new codecs increase the computational cost.

Similarly to Artificial Intelligence (AI), several types of hardware are commonly used in video transcoding applications:

1. ASICs (Application Specific Integrated Circuits): Video ASICs are specialized hardware accelerators designed specifically for video processing tasks, such as video encoding/ decoding / transcoding. They are sometimes called VPU (Video Processing Unit) or VCU (Video coding Unit). They significantly improve performance over traditional CPUs, particularly for high-resolution or high-frame-rate video. Alphabet, the mother company of Google and YouTube has designed its own ASICs : codename Argos. For a system with 20 VCUs, the TCO has decreased by a 33x factor for VP9 encoding, and throughput increased by a 100x factor!

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Facebook/ Meta made the same move to specific hardware. They partnered with silicon vendors to [design custom ASICs](https://engineering.fb.com/2019/03/14/data-center-engineering/accelerating-infrastructure/).

The electric consumption of an ASIC is around a few watts.

2. FPGAs (Field-programmable gate arrays): FPGAs are programmable hardware devices that can be customized for specific applications, including video transcoding. They can provide significant performance improvements over traditional CPUs for certain types of video transcoding tasks, such as simultaneously encoding or decoding multiple video streams. Twitch is using an [FPGA from Xilinx](https://blog.twitch.tv/en/2018/12/19/how-v-p9-delivers-value-for-twitch-s-esports-live-streaming-35db26f6322f/).

The electric consumption is around 25 W for a PCIe card

3. Graphics processing units (GPUs): GPUs are another type of hardware. This hardware is more generic than FPGA and ASIC, but specialized video encoders/decoders are added to these cards. Nvidia was the first company to build a datacenter GPU for transcoding. Intel enters the market in late 2022.

The electric consumption is generally around 70W.

Regarding energy consumption ASICs > (are better than) FPGAs > GPU > CPU. This order is the same for the transcoding speed.

**Reducing the carbon footprint of video transcoding : api.video’s approach**

At api.video we are really focused on the energy consumption of our transcoding pipeline. We want to minimize the ecological impact of video transcoding while keeping the transcoding speed high. The infrastructure team has performed multiple benchmarks, and here are the results.

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Those numbers are based on public information (marketing sheet) for the GPUs and FPGAs and real benchmarks for the CPU and ASICs. The quality of the video's output could change the results slightly, but we clearly see a winner regarding power efficiency. In a future blog post, we will share precise benchmarks about the ASICs vs. CPU battle with you. Stay tuned.

Along this article, we have seen the resurgence of dedicated hardware in computer science is driven by a growing need for high-performance computing, particularly in fields like artificial intelligence, machine learning, and video transcoding. Dedicated hardware can provide better performance and energy efficiency than traditional general-purpose computing units, making it an attractive option for many applications. As computing continues to evolve, we will likely see more specialized hardware being developed for specific tasks, driving innovation and progress in the field. 

Our video API allows you to add videos to your products while prioritizing the reduction of your carbon footprint. [Join us](https://api.video/) and take part of the transition to an environmentally responsible video infrastructure.

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Have you ever uploaded a video on YouTube and wondered how your video is magically available in 5+ different qualities and 5+ different sizes? It’s not magic ✨ it’s *video transcoding*. 

Well, it’s kind of magic 🌟

In this article, we’ll tell you everything you want to know about video transcoding — from what it is, to its importance in video streaming and [video APIs](https://api.video/). With that said, let’s get started! 🚀

## The difference between video encoding and video transcoding 

Encoding and transcoding sound very similar but, in fact, are two very [different processes](https://api.video/blog/video-trends/what-is-the-difference-between-encoding-and-transcoding/). To learn about video transcoding, let’s first describe video encoding.

### What is video encoding? 

[Video encoding](https://api.video/blog/video-trends/guide-to-video-encoding-video-codec/) digitally compresses raw video from a digital or analog camera. You almost never have to worry about video encoding as your device encodes all videos automatically. The video you recorded of your dog this morning and stored on your phone - it is there on your phone because of video encoding. Similarly, the video that you shared with your friend using WhatsApp wouldn't have been be possible to share without video encoding.

In shirt, encoding is necessary for creating manageable file sizes.

### What is video transcoding?

Video transcoding re-compresses an encoded file which means that video transcoding cannot happen without an initial video encoding.

Transcoding, compresses a file from one digital [codec](https://api.video/blog/product-updates/every-video-format-codec-and-container-explained/) to another. Being able to play videos across a wider range of devices, or to improve playback experience are two of the many reasons that we transcode videos today.

An example of video transcoding is to transcode an encoded AV1 video to an H264 video codec. Why do you need to do that, you may ask? AV1 codec, despite being an efficient codec, is not supported on a wide range of devices. H264 codec on the other hand, is supported on almost all of the devices out there.

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## Types of video transcoding

When transcoding a video file, some data is lost, which is what we call lossy—the greater the compression, the smaller the file size and the lower the quality. Let’s now look at three main types of video transcoding.

### 1. Lossy-to-lossy

It occurs when one type of lossy data is converted into a different kind of lossy data. Having a file of lower quality before transcoding means the quality will drop even further during the process. Reducing the bitrate for use on portable devices is one of the justification for this transcoding method.

### 2. Lossless-to-lossy

This transcoding reduces quality loss relative to lossy-to-lossy methods while creating files with manageable sizes for mobile devices. To make good use of this transcoding technique, you will need to save archives of files that have been compressed losslessly.

### 3. Lossless-to-lossless

This type of transcoding involves using improved compression to shrink a file without altering its quality. This transcoding method saves quality when switching between formats, but the generated files are sometimes too large to transfer to mobile devices.

## Why is video transcoding used?

The use of video transcoding is based on requirements. Here we can look at some of the top reasons for using video transcoding:

- **Adaptive bitrate streaming (ABR streaming)** - ABR streaming ensures the best video experience on a wide range of devices depending on your internet connection’s speed and stability. Transcoding is a crucial intermediate step to get the highest quality streaming output based on HLS or DASH. For example, video playback on Youtube could be switching between 1080p and 720p versions of the video stream, depending on your device and connection. This leads to the fewest buffering instances and higher-quality playbacks.

- **Limited storage capacity** - The word cloud is one of the biggest tricks played by marketers. It allows us to imagine a world with an infinite storage capacity for data. This is far from reality. Cloud is some hardware located in a room somewhere. No matter where you store your videos, cloud or not, you are using certain storage space. Transcoding is the key to maximizing your storage capacity, resulting in lower carbon footprints. Yes, actual carbon footprints.

- **Quality of user experience** - Transcoding is about more than just reducing a file size. Transcoding is also used to improve the playback experience.

- **Multiple target devices, browsers, and applications** - Transcoding is used for video compatibility amongst different devices, browsers, and applications.

## Who uses video transcoding?

Many individuals and businesses use video transcoding.

- Video streaming APIs for businesses and developers such as [api.video](https://api.video/). Video transcoding is an integral element of an adaptive video streaming workflow to get video content ready for a delivery protocol — like the current industry standard, HLS or DASH — that can play on as many devices as possible.
- Video-On-Demand (VOD) services such as Netflix, Hulu, and Youtube use video transcoding on large scales.
- Content Management Systems (CMS) targeting multiple devices with a variety of video formats and sizes.
- Video Editors. As a video editor, you may prefer to work on the highest quality video codec, and once you're done editing, you could transcode your video to other codecs for delivering to a wider range of devices.

## Video APIs and video transcoding

As one of the more important users of video transcoding, video APIs are beneficial for businesses, both big and small, to create and host on-demand videos. 

[High-quality video APIs](https://api.video/) such as api.video can change your video into HTTP Live Streaming (HLS) for smooth streaming at high speeds anywhere in the world without you needing to worry about doing the process yourself. For the highest possible video quality on any connection, it handles the nitty-gritty of transcoding in different qualities for ABR streaming over HLS while keeping the best playback quality on a wide range of devices.

## Final thoughts

New gadgets are being introduced to us every day. This means there will be more cameras with greater capturing capacities and there will be more screens with higher display qualities.  At the same time, the video world is booming, and more and more businesses need to reach out to users with all sorts of devices and bandwidth. 

Transcoding is not magic, but it certainly has a magical aspect to it. It makes it possible for us to connect through high-quality videos.

Hopefully, now you know more about transcoding and why it’s used in our industry. 

Would you like to start your video journey today? 🎬 Go ahead and start testing in the [api.video](https://dashboard.api.video/register) dashboard for free.

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