api.video lets you quickly and securely deliver on-demand and live-stream videos directly from your website, software, or app. 

api.video

Glossary-Startup-time.svg

## What is video startup time?
Video startup time is the time from pressing "play" to the video starting to move.

When a video takes a long time to startup, viewers begin to abandon, and go do something else - maybe even abandoning the site. So it is imperative minimize the amount of time between pressing play and having the video begin playback.

## The video tag
In the video tag, you can specify [autoplay](https://api.video/what-is/autoplay/) to esure fast startup. But for videos where a user will press a button to start the video, using the [preload](https://api.video/what-is/preload/) attribute can ensure video locally available for playback.

## Adaptive streaming
api.video uses [adaptive bitrate streaming](https://api.video/what-is/adaptive-bitrate-streaming/) (ABS). ABS has multiple versions of the videos at different sizes.  One way that ABS streams can ensure fast startup is to start the video playback with a lower quality video (240p video is much smaller in KB than a 1080p video).  The first few seconds arrive very quickly, the player is able to calculate the network speed, and then subsequent segments are shown at a higher quality. Many video streaming services use this feature, as the first few seconds of a move or TV show are often a black screen anyway.

## CDN
A robust [CDN](https://api.video/what-is/content-delivery-network/) network ensures that all video segments are stored on the edge of the internet near the viewers - reducing the round trip time for each request.  The less time spent in transit is less time in delay for the video to begin on the users' device.

Glossary-text-to-image.svg

## What is Text to Image?
Text to Image is an AI-powered technology that generates visual content based on textual descriptions. This innovative application combines [Natural Language Processing](https://api.video/what-is/natural-language-processing/) with advanced image generation techniques to transform written words into corresponding images. It's a key component of [Generative AI](https://api.video/what-is/generative-ai/), enabling the creation of visual content without traditional drawing or photography.

## Key Components of Text to Image Systems
Text to Image systems typically involve several interconnected components that work together to transform textual descriptions into visual content. At the core is the text understanding component, which is responsible for parsing and interpreting the input text, extracting meaning and context. This understanding then informs the concept mapping, where the textual descriptions are linked to the appropriate visual concepts.

The image generation component is tasked with creating the actual visual content based on the interpreted text. This process may involve the use of Generative Adversarial Networks (GANs) to generate realistic images, or Diffusion Models that gradually denoise random noise to form coherent visuals. Transformer architectures can also play a role in enabling more complex understanding of the relationships between text and images.

To further refine the generated content, the system may incorporate style application techniques, allowing for the addition of specific artistic styles or visual characteristics. Finally, an iterative refinement process may be employed to improve the quality and coherence of the final generated image.

## Techniques Used in Text to Image
Several AI techniques are crucial for powering the capabilities of Text to Image technology. Natural Language Understanding is essential for extracting meaning and context from the input text, providing the foundation for the concept mapping and image generation processes. Generative Adversarial Networks and Diffusion Models are employed to create realistic images based on learned patterns and gradual denoising, respectively.

Transformer architectures, with their ability to capture complex relationships between textual and visual data, have also become an integral part of Text to Image systems. Additionally, style transfer techniques enable the application of specific artistic styles or visual characteristics to the generated content, further enhancing the creative possibilities.

## Applications of Text to Image
Text to Image technology has numerous potential applications across various industries and domains. In content creation, it can be used to quickly produce illustrations, concept art, or visual aids for various media, from publications to presentations. In the realm of design and prototyping, the technology can rapidly visualize ideas for product design, interior decoration, or fashion, accelerating the creative process.

Accessibility is another key area of application, as Text to Image can convert textual descriptions into visual representations, enhancing understanding and comprehension for users with different needs. Educational tools can also leverage this technology to create visual aids that support learning and knowledge retention.

The marketing and advertising industry can benefit from Text to Image by generating custom visuals for campaigns based on textual briefs, tailoring the content to specific messaging and brand guidelines. Additionally, the technology can provide artists and designers with visual starting points based on textual ideas, serving as a source of creative inspiration.

As Text to Image systems continue to evolve, their applications will likely expand, offering new possibilities for content creation, visualization, and user experience across a wide range of industries and applications.

## Challenges and Considerations
Text to Image technology faces several challenges:
- Accuracy and Relevance: Ensuring the generated images accurately represent the input text.
- Artistic Quality: Producing images that meet professional standards of composition and aesthetics.
- Handling Complex Descriptions: Accurately interpreting and visualizing intricate or abstract textual descriptions.
- Bias and Representation: Addressing potential biases in image generation, ensuring fair and diverse representation.
- Ethical and Legal Issues: Navigating copyright concerns and potential misuse of the technology.

## The Future of Text to Image
We can expect several exciting developments such as: Higher Resolution and Quality, Enhanced User Control, Multimodal Input, Real-time Generation or even 3D and Animation Integration.

As Text to Image technology evolves, it has the potential to revolutionize visual content creation across various industries, from entertainment to education. However, it will also require careful consideration of ethical implications and the development of guidelines for responsible use, particularly in maintaining the value of human creativity and addressing issues of authenticity in visual media.

Glossary-text-to-video.svg

## What is Text to Video?
Text to Video is an AI-powered technology that generates video content based on textual input. This cutting-edge application combines [Natural Language Processing](https://api.video/what-is/natural-language-processing/) with [Computer Vision](https://api.video/what-is/computer-vision/) and video generation techniques to transform written descriptions into visual narratives. It represents a significant advancement in [Generative AI](https://api.video/what-is/generative-ai/), enabling the creation of video content without traditional filming or animation processes.

## Key Components of Text to Video Systems
Text to Video systems typically involve several key components:
1. Text Understanding: Parsing and interpreting the input text
2. Scene Planning: Determining the sequence of visual elements
3. Asset Generation: Creating or retrieving visual assets based on the text
4. Video Composition: Assembling the assets into coherent video sequences
5. Audio Generation: Creating or adding appropriate audio elements

## Techniques Used in Text to Video
Several AI techniques are crucial for Text to Video technology:
- Natural Language Understanding: Extracting meaning and context from the input text.
- Image Generation: Creating individual frames or elements using techniques like [GPT](https://api.video/what-is/gpt/) for images or other generative models.
- Sequence Modeling: Ensuring coherence and continuity across the generated video frames.
- Style Transfer: Applying specific visual styles to the generated content.
- Temporal Consistency: Maintaining consistency in object appearance and movement across frames.

## Applications of Text to Video
Text to Video technology has numerous potential applications. In content creation, it can rapidly produce promotional videos, educational content, or storytelling visuals from scripts, accelerating the creative process. For prototyping, it can quickly visualize concepts for filmmaking or advertising, enabling faster iteration. The technology can also improve accessibility by converting text-based content into engaging video formats.

Text to Video can generate personalized video content based on user descriptions, opening up new possibilities for customized experiences. In virtual production, it can assist in pre-visualization for film and TV. Furthermore, it can create visual aids and animations to supplement educational materials, enhancing learning. As the technology evolves, its applications will likely expand, transforming how we create and interact with video content.

## Challenges and Considerations
Text to Video technology faces several challenges:
- Quality and Realism: Generating high-quality, realistic video content that matches the complexity of the input text.
- Temporal Coherence: Ensuring consistent object appearance and movement across frames.
- Stylistic Control: Providing users with fine-grained control over the visual style of the generated video.
- Ethical and Legal Issues: Addressing concerns about copyright, deep fakes, and potential misuse of the technology.
- Computational Requirements: Managing the intensive processing needs for real-time or near-real-time video generation.

## The Future of Text to Video
As Text to Video technology advances, we can expect several exciting developments. Improvements in quality and realism will enable the generation of increasingly photorealistic and complex video content. Enhanced user control through more intuitive interfaces will allow for greater customization of video characteristics and styles.

Real-time generation capabilities could enable near-instantaneous video creation from text input, opening up new applications. Incorporating multimodal inputs like voice, sketches, or images could lead to more nuanced and contextually-aware video generation. Seamless integration with video editing software and content management systems would further expand the reach and utility of this transformative technology. The future of Text to Video holds immense potential, revolutionizing how we create, manipulate, and interact with video content.

As Text to Video technology evolves, it has the potential to revolutionize content creation, making video production more accessible and efficient across various industries. However, it will also necessitate careful consideration of ethical implications and the development of guidelines for responsible use.

Glossary-Thumbnail.svg

## What is a thumbnail?
A thumbnail, sometimes referred to as a thumb, is a reduced in size image that acts as a preview of a video or image. For images, it's a small, compressed version of the image. However for video, it could be a frame from the video, or it could be a custom image that has to do with the video content in some way. Thumbnails are intended to make it easier for users to visually locate video clips or images they need for their work or viewing purposes. They can also, as with YouTube, be used to attract more viewers by showing an interesting scene from the video content. 

If you're looking for information about the HTML5 attribute, it's called poster and the details are posted at [poster](https://api.video/what-is/poster/).

Blog_MobilePlayer.png

## Origin of the term thumbnail
The term thumbnail came into existence for still images. It's a reference to the human thumbnail and is used to describe the small size of an image, as in it's so small it's the size of a thumbnail. This term was used in this way to refer to images back in the 17th century. However, people also applied it to anything where you were creating a smaller preview of something. It could be a painting or an essay. 

## Why are thumbnails important?
Thumbnails help people decide if they want to watch your content. If the thumbnail looks interesting or outrageous, people will sometimes click a video just to see what's going on and how that scene arose. Thumbnails can also give video content a completed, professional and polished look. You can choose a good image of the speaker in the video for example, or create a custom image that has to do with the content or is a title card. 

Great thumbnails convey what's going to happen in the video, set the tone of the video and include your brand. 

## Thumbnails and api.video
api.video provides two different ways you can add thumbnails to your videos, for a professional and polished look. You can either grab the thumbnail from a frame in the timeline and automatically have the api.video API add it to the front of your video, or you can add a custom image as the thumbnail. 

Learn how to [create a thumbnail for a video with Python and FFMPEG here](https://api.video/blog/tutorials/automatically-add-a-thumbnail-to-your-video-with-python-and-ffmpeg/). You won't have to do this with api.video's API. 

You can also see [how to pick a thumbnail using the api.video API here](https://api.video/blog/tutorials/choose-a-thumbnail-with-api-video-s-pick-a-thumbnail-endpoint/). 

## What is transcoding?
Transcoding is the process of taking already encoded (or transcoded) content, decompressing it and then using different codecs to alter or recompress it. It's labor intensive because you are taking every part of your video and audio and recompressing it. During this process, depending on what codecs you use, you may lose data. 

## What types of transcoding are there?
There are three types of transcoding you will encounter: 

* **Lossy-to-lossy** - This is where you take a codec that is lossy and transcode to another format that uses a lossy codec. You will lose lots of data during this process, and transcoding will lower the quality of your video.
* **Lossless-to-lossy** - This is where you take a codec that preserves every detail of a video and transfer it to one that loses data. The trade off results in something useful like faster decompression for playback, or a smaller file.
* **Lossy-to-lossless** - You cannot regain lost quality, so when transcoding in this situation, you retain all the data that was in the lossy compression without further degradation of the file. 

## What are the goals of transcoding?
The goals of transcoding are dependent upon your use case. The main reason to transcode video is because you want to use it in a new scenario with a codec that's optimized for that new scenario. For example, say you want to edit a video. You would probably want a codec that's good quality, but which lets you easily move backwards and forwards through the video so you can quickly find the parts of the video you want to clip and edit. A great example of a codec for this purpose would be ProRes. This codec doesn't work for playback with a lot of devices though. So after you're done editing, you would want to transcode to another codec that works for playback. 

Other scenarios might be something like wanting to store a video for later use. When you store a video, if it's for a project, you might want to store the highest quality possible. Then when you get the video out later to edit again or use in a project, it's got as much information available as possible. Or, maybe you're storing a video just for reference purposes and you don't have a lot of space. Then you might want a codec that compresses efficiently but the video quality is lower. That would be enough for reference later. 

## Transcoding and api.video
api.video transcodes your video to HLS for fast, high quality playback across the internet. If you're interested in learning more about transcoding, check out our article [Encoding and Transcoding - What's the Difference?](https://api.video/blog/video-trends/what-is-the-difference-between-encoding-and-transcoding/). And if you want to give api.video a try, you can sign up [here](https://dashboard.api.video/register)

Glossary-run-length-encoding.svg

## What is run-length encoding?
Run-length encoding (RLE) is a lossless compression method where sequences that display redundant data are stored as a single data value. This value represents the repeated block, and shows how many times it appears in the image. During decompression, the image can be reconstructed exactly from this information.

This type of compression works best with simple images and animations that have a lot of redundant pixels. It is useful for black and white images in particular. For complex images and animations that do not have many redundant sections, RLE can make the file size bigger rather than smaller. Therefore it is important to understand the content and whether this algorithm will help or hinder compression.

A graphic showing how run-length encoding works by compressing a string of the same letter down to 1 of the letter and a number representing the number of times the letter appears in a row.

run-length.png

## History of run-length encoding
This technique was first patented by Hitachi in 1983, and was regularly used when transmitting analog television signals, even as early as 1967. It is less popular today because there are other more advanced options available. You can still find it in use for color fax machines, icons, line drawings, and simple animations. The method is also used in TIFF and PDF files.

## Run-length encoding and LZ77
LZ77 is an algorithm that compresses data in a similar way to RLE. It replaces repeated occurences of data with a reference to a single copy of that data that exists earlier in the uncompressed stream. If there is a match to a chunk of data, then LZ77 encodes a match that represents the distance between the two chunks of data and the length of each chunk. To find matches, LZ77 has to track a chunk of recent data, which can be of varying sizes. The bigger the chunk of recent data, the bigger a chunk it can search through for a match. LZ77 is also called sliding-window compression because it is viewing a chunk of data that can be of varying sizes to find matches. Pairs can repeat multiple times, which makes LZ77 similar to RLE, just a bit more complex.

The concept used to create RLE also appears in LZ78, and other popular compression methods we still use today, like GIF and DEFLATE. The DEFLATE algorithm appears in PNG and ZIP.

## Drawbacks of run-length encoding
The main drawback of RLE is that it is best suited for simple and repetitive data only. Here are a few more aspects to keep in mind when working with RLE:
- The original data is ot instantly accessible, you must decode everything before you can access anything.
- You cannot tell how large the decoded data will be. This can cause problems when you have limited space to decompress the file in. In this case, you can either store the size of the original data as metadata, or use this compression method with a dynamic buffer and enough space to decompress the content in.

&nbsp;  

Learn more about the history of video compression and the different algorithms that have appeared over time in our blog article about [The History of Video Compression Standards, From 1929 Until Now](https://api.video/blog/video-trends/the-history-of-video-compression-starts-in-1929/).

## What is RTMPS (Real-Time Messaging Protocol Secure) ?
RTMPS, which stands for Real-Time Messaging Protocol Secure, is an extended version of the [RTMP (Real-Time Messaging Protocol)](https://api.video/what-is/rtmp-real-time-messaging-protocol/) that adds a layer of security through SSL/TLS encryption. It is primarily used for streaming live audio, video, and data over the Internet. RTMPS ensures that the content being streamed is protected from eavesdropping and tampering during transmission.

api.video supports RTMPS, if you are interested, check the blog article about [how to set up RTMPS using api.video](https://api.video/blog/product-updates/rtmps-livestreaming/), you can either start your livestream with RTMPS through the dashboard or through the API.

## Key Components of RTMPS
At its core, RTMPS builds upon the RTMP base, adding a crucial SSL/TLS layer for encryption and security. The protocol employs a handshake process to establish a secure connection between the client and server. Once established, it uses chunking to break data into manageable pieces for transmission and multiplexing to allow multiple streams of data to be sent simultaneously.

The RTMPS process begins with the client initiating a connection to the server over SSL/TLS. This is followed by an SSL/TLS handshake, where the client and server establish a secure encrypted connection. Within this secure tunnel, the standard RTMP handshake then occurs. After successful handshakes, one or more streams are created for data transmission. Audio, video, and other data are then sent in real-time through this encrypted connection. Either party can securely close the connection when streaming is complete.

## Applications and Use Cases
RTMPS finds wide application in various streaming scenarios. It's commonly used for live video broadcasting, enabling secure streaming of events, webinars, or personal broadcasts. In video conferencing, it ensures private and secure communication for real-time video calls. The gaming industry utilizes RTMPS to protect live game streams from potential security threats. In the education sector, it secures live educational content delivery to students in e-learning environments. RTMPS also plays a role in IoT device communication, enabling secure real-time data transmission from Internet of Things devices. Additionally, it facilitates secure internal broadcasts within organizations for corporate communications.

## Advantages and Considerations
The primary advantage of RTMPS is its enhanced security, protecting sensitive content from unauthorized access during transmission. It's also highly compatible with existing RTMP infrastructure, requiring minimal changes for implementation. RTMPS maintains the low-latency benefits of RTMP while adding a layer of security.

However, there are considerations when implementing RTMPS. The encryption process can add some computational overhead, which may impact performance in resource-constrained environments. Proper management of SSL/TLS certificates is crucial for maintaining security. Additionally, specific firewall configurations may be necessary to allow RTMPS traffic, which can be a consideration in some network environments.

## Future Prospects
As video streaming continues to grow in importance, RTMPS is likely to evolve. We can expect to see integration with new codecs, allowing it to work efficiently with emerging video and audio compression technologies. Efforts to enhance performance and reduce the overhead of encryption for even lower latency are likely to continue. As security becomes increasingly critical, broader adoption across various platforms is anticipated.

However, it's worth noting that while RTMPS is currently widely used, newer protocols like [SRT (Secure Reliable Transport)](https://api.video/blog/product-updates/SRT-livestreaming/) may eventually supersede it in some applications. Nonetheless, RTMPS remains a crucial protocol in the world of real-time video streaming, balancing the needs for security, low latency, and compatibility with existing systems.

large_run-length.png

medium_run-length.png

small_run-length.png

thumbnail_run-length.png

Glossary-RTMPS.svg

Glossary-RTMP.svg

## What is RTMP?
RTMP stands for Real Time Messaging Protocol and for many years, it's been the standard for streaming video over the internet. What really put RTMP on the map was Adobe's Flash Player. Flash became so popular that browsers and encoders everywhere rushed to support it, which included support for RTMP. Because of this, today most encoders and media servers can support RTMP. That's why it's still the standard for streaming video and live streaming. 

The RTMP streaming protocol is TCP-based (Transmission Control Protocol) and designed to maintain constant, low-latency connections between a video player and server. The design allows RTMP to provide smooth streaming for viewers. And because RTMP uses TCP, it's very reliable. TCP uses a three way handshake when transporting data - the client contacts the server, the server accepts and responds, then the client acknowledges the response. At this point a session is opened, and RTMP can start streaming video. 

RTMP was popular up through the early 2010s, but then came the rise of HLS (created in 2009) and adaptive bitrate streaming. Adaptive bitrate streaming allows you to optimize content based on available connectivity and viewer devices. This type of streaming requires the use of HTTP-based protocols. So today, RTMP is used to move between servers, but when it's time to deliver content to the viewer, the last mile of delivery usually uses HLS and adaptive bitrate delivery. 

RTMP will likely continue to be popular because so many encoders and servers still support it, but over time, other protocols will replace it. If you want more details about the benefits and drawbacks of RTMP, check out api.video's blog post: [What is RTMP and Why Do We Use it for Live Streaming?](https://api.video/blog/video-trends/what-is-rtmp-and-why-do-we-use-it-for-live-streaming/)

For Live streaming, check this article [comparing RTMP to SRT](https://api.video/blog/video-trends/srt-vs-rtmp/).

## What is an API rate limit?
An API rate limit is a threshold placed on the number of API calls that can be placed over a period of time.  For example: you may only mayke 3 login attempts an hour, or make 150 calls to Twitter every 5 minutes.

API services have upper limits on the number of requests that can be handled every second (millisecond).  If one user (or several users) begin taking a substantial portion of the available bandwidth of the API - the API will fail to work for others,  By placing limits on individual users, API developers can protect their infrastructure from too many requests.

Rate limits can also affect bandwidth. For those in the USA, your "unlimited 4G data" mobile data plan is typically 25 or 40 GB at 4G speeds, and then you are rate limited - your plan is throttled to 2g for the rest of the month.

## Rate limits at api.video

We currently have no rate limits on our API, but we do monitor for accounts that are using excessive numbers of calls, and will work with that developer to mitigate their usage.  We are considering rate limits on video uploads, but these are still under development.

### Rate limits on video playback

There are no limits on video playback. If there was ever an unprecedented global "viral video" at api.video, we would be able to handle the load.  On the first request from our backend, api.video would deliver the video to our global video CDN, where it would be cached.  The delivery from our CDN will be fast an efficient, and since it is abstracted from our infrastructure - will not impact and other video playback from api.video.

Video on demand

Live streaming

Analytics

Video infrastructure

Video player

AI features

See all features

Online learning & Corporate training

Social & Entertainment

Marketplace & E-commerce

Communication & UGC

Generative AI

Quickstart guides

Documentation

Ecosystem catalog

Clients & SDKs

Community

Blog

Demos

Customer success stories

Help center

Startup time

What is video startup time?

The video tag

Adaptive streaming

CDN