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

api.video

Glossary-Muted.svg

## What is the muted attribute in HTML5?
The muted attribute is used in the HTML5 video player to tell the browser that playback should begin without audio.  When the attribute is omitted (or set to false), the video will begin playback with audio.

The muted attribute is most often paired with [autoplay](https://api.video/what-is/autoplay/). Many browsers will not autoplay a video with audio playback, so the combination of  ```muted autoplay``` will ensure that videos will autoplay in most browsers.

If you intend to have a silent video, you might want to consider removing the audio track from the video itself, rather than send the audio to each viewer and have the browser hide the audio.

large_1200px-muteicon-svg.png

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small_1200px-muteicon-svg.png

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1200px-muteicon-svg.png

### Muted with api.video

* [PlayerSDK](https://docs.api.video/sdks/player/apivideo-player-sdk): you can set the video to muted on loading:

```
   player.loadConfig({
      id: "<VIDEO_ID>",
      muted: true,
      hideControls: true,
   });
```


Glossary-Natural-Language-Processing.svg

## What is Natural Language Processing?

Natural Language Processing (NLP) is a branch of [Artificial Intelligence](https://api.video/what-is/artificial-intelligence/) that focuses on the interaction between computers and human language. In the context of video technology, NLP plays a crucial role in understanding, generating, and manipulating text and speech associated with video content. It bridges the gap between human communication and computer understanding, enabling more intuitive and powerful video-related applications.

## Key Components of NLP in Video Technology
NLP can play a vital role in video technology. It involves several key components that enable a wide range of video applications. Speech recognition converts spoken words in videos into text, while text analysis helps understand the meaning and context of written or transcribed content. Sentiment analysis determines the emotional tone of language used in videos. Machine translation facilitates cross-language accessibility by translating video content between languages. Text generation creates human-readable descriptions or summaries based on video content or metadata.

These NLP capabilities enable a wide range of video applications, from automated captioning and subtitling to advanced video search and personalized recommendations, making them an integral part of the video technology landscape.

## NLP Techniques Relevant to Video
Several NLP techniques are particularly relevant to video technology:
- Tokenization: Breaking down text or speech into individual words or subwords.
- Part-of-Speech Tagging: Identifying grammatical components in sentences.
- Named Entity Recognition: Identifying and classifying named entities (e.g., people, places, organizations) in text.
- Semantic Analysis: Understanding the meaning and relationships between words and phrases.
- Language Modeling: Predicting the probability of sequences of words, crucial for generating coherent text.

## Applications in Video Technology
NLP has transformed numerous aspects of video technology:
- Automatic Captioning and Subtitling: Generating accurate text captions for video content, including speaker identification and timing alignment.
- Content Summarization: Creating concise text summaries of video content, useful for video indexing and quick reference.
- Video Search and Retrieval: Enabling natural language queries to find specific moments or topics within large video libraries.
- Script Analysis and Generation: Assisting in screenplay writing or generating dialogue for animated characters.
- Voice-Controlled Video Editing: Allowing editors to use natural language commands to manipulate video content.
- Metadata Generation: Automatically creating descriptive tags and categories for video content based on audio and visual analysis.

## Challenges and Considerations
While powerful, NLP in video technology faces several notable challenges. Adapting to various languages and cultural contexts in global video content requires robust multilingual and multicultural understanding. Maintaining context retention over long video sequences or across multiple scenes can also prove difficult.

Additionally, accurately processing the informal language, colloquialisms, and slang often used in videos presents a significant hurdle. Integrating language understanding with the visual elements in the video is another key challenge. Importantly, ensuring the responsible use of NLP technologies, especially in sensitive content analysis, raises crucial privacy and ethical concerns that must be addressed. Overcoming these multifaceted challenges will be crucial as NLP continues to play an increasingly integral role in video technology.

## The Future of NLP in Video
As NLP techniques continue to advance, we can expect several exciting developments in video technology:
- Multimodal NLP: Closer integration of language processing with visual and audio analysis for more comprehensive video understanding.
- Real-Time Translation and Dubbing: Instant, high-quality translation and lip-synced dubbing of video content into multiple languages.
- Advanced Conversational Interfaces: More natural and context-aware voice control for video editing and navigation systems.
- Emotion-Aware Content Creation: NLP models that can understand and generate emotionally nuanced scripts or captions.
- Personalized Content Adaptation: Tailoring video content, including dialogue and text elements, to individual viewer preferences and comprehension levels.  

&nbsp;  

As Natural Language Processing continues to evolve, it will play an increasingly crucial role in enhancing the creation, distribution, and consumption of video content. By bridging the gap between human language and video technology, NLP will enable more intuitive, accessible, and personalized video experiences across various platforms and applications.

Glossary-neural-networks.svg

## What are Neural Networks?

Neural Networks are a fundamental component of [deep learning](https://api.video/what-is/deep-learning/), inspired by the structure and function of the human brain. In the context of video technology, neural networks process vast amounts of visual data to perform tasks such as object recognition, scene understanding, and even video generation. These networks consist of interconnected nodes (neurons) organized in layers, which work together to analyze and interpret complex patterns in video content.

## Types of Neural Networks in Video Processing
Several types of neural networks are particularly relevant to video technology:
1. Convolutional Neural Networks (CNNs): Excel at processing grid-like data, making them ideal for analyzing individual video frames.
2. Recurrent Neural Networks (RNNs): Specialize in sequential data, allowing them to process temporal aspects of video.
3. Long Short-Term Memory Networks (LSTMs): A type of RNN that can capture long-term dependencies in video sequences.
4. Generative Adversarial Networks (GANs): Used for generating new video content or enhancing existing footage.

## How Neural Networks Process Video Data
Neural networks process video data through a series of transformations:
- Input Layer: Receives raw video data, typically as a sequence of frames.
- Hidden Layers: Apply various operations (e.g., convolutions, pooling) to extract features from the input.
- Output Layer: Produces the final result, which could be a classification, a generated frame, or other video-related information.  

&nbsp;  

During training, the network adjusts its internal parameters through backpropagation, minimizing the difference between its predictions and the actual desired outputs.

## Applications in Video Technology
Neural networks have revolutionized numerous aspects of video technology. They enable video classification, automatically categorizing videos based on content, genre, or style. Object detection and tracking algorithms can identify and follow specific objects or people across video frames. Neural networks also play a crucial role in video enhancement, allowing for upscaling resolution, reducing noise, or even colorizing black and white footage.

Additionally, they are employed in video generation, creating new video content based on learned patterns or textual descriptions. Neural networks have even been applied to video compression, developing more efficient algorithms for video storage and streaming. Finally, facial recognition techniques leverage neural networks to identify and analyze faces in video footage for various applications.

## Challenges and Considerations
While powerful, neural networks in video technology face several challenges:
- Computational Requirements: Training and running complex neural networks on video data can be computationally intensive.
- Data Hunger: Neural networks often require large amounts of diverse, high-quality video data for effective training.
- Interpretability: The complex nature of neural networks can make it difficult to understand their decision-making processes.
- Overfitting: Networks may become too specialized to their training data, performing poorly on new, unseen video content.

## The Future of Neural Networks in Video
As neural network technologies continue to advance, we can expect several exciting developments. Improvements in hardware and network architectures will enable more sophisticated neural network operations to be performed in real-time, even on mobile devices. Advancements in 3D and 4D convolutions will allow networks to better understand spatial and temporal relationships in video.

Future networks will likely integrate the processing of visual, audio, and textual data more seamlessly, enabling more comprehensive video understanding through multimodal approaches. 

Additionally, self-supervised learning techniques could reduce the need for large amounts of labeled data, making it easier to train networks for specific video tasks.

Neural networks will undoubtedly offer new possibilities for content creation, analysis, and user experience. However, addressing their challenges related to efficiency, interpretability, and ethical use will be key to realizing their full potential in the video industry.

Glossary-OTT.svg

## What is Over the top (OTT)?

Over the Top (OTT) describes how a video can be delivered to customers directly over the internet.  The term derives from the delivery "on top" of basic internet protocols, bypassing the traditional providers of video: TV, cable and satellite. OTT video streaming has revolutionised the way movies and shows are watched by consumers.

There are many OTT providers, including Netflix, Hulu, Disney+ and more.  Many "traditional broadcasters have created their own OTT services (HBO Max, Peacock (NBC), etc). 

OTT delivery has led to new viewing strategies by customers. Now that shows are available on demand, consumers are no longer bound to a specific time or day of the week for a TV show. As a result, viewing patterns have changed in a way that is more convenient to the viewer. Further, the concept of "binging" a show - watching an entire season in the course of a few hours or days has also arisen from OTT video streaming.

## OTT at api.video

api.video hosts and streams video on demand.  If you have OTT content that you want delivered quickly and efficiently worldwide, we can host and deliver that content for you.  We also offer "private videos'' that only allow authenticated users access to the movies, and prevent the sharing of the video urls.


## What is the video poster HTML5 attribute? 
The video poster is the HTML5 attribute for the video tag that defines the image that appears in front of a video before it starts playing.

If no poster tag is defined in the video tag, the browser will display the first frame of the video. Since many videos begin with a black screen, this is not visually appealing, so a different screenshot from the video, or perhaps an unrelated image is used.

The poster tag points to an image: 

```
poster=https://example.com/poster.jpg
```

There is no way to serve alternative formats with the poster tag, so typically a format that is supported in many browsers (like jpg or png) are used.  

&nbsp;  

> Note: The poster image is downloaded before the video, so if your video is going to autoplay, this will block the video from loading.  Additionally, creating a poster of the first frame of the video is redundant, as the player will do this anyway, and this results in that frame being downloaded twice.

## Poster at api.video
- The poster tag is called *thumbnail* at api.video.  
- The default thumbnail is selected from the "middle frame" of the video (example: the 15 minute point of a 30 minute video).

## Changing the thumbnail
- Set a frame from the video: The [pick a thumbnail](https://docs.api.video/reference#patch_videos-videoid-thumbnail) endpoint sends a timestamp where the image should be extracted from the video and set as the poster.
- [Upload a Thumbnail](https://docs.api.video/reference#post_videos-videoid-thumbnail): Upload a jpg image to be used as the video thumbnail.


Glossary-Multimodal.svg

## What is a Multimodal AI

Multimodal AI refers to artificial intelligence systems that can process and integrate information from multiple types of input or "modalities." In the context of video technology, this typically involves combining visual data with other forms of input such as audio, text, or even sensor data. Multimodal systems aim to achieve a more comprehensive understanding of content by leveraging the complementary information provided by different modalities.

## Key Modalities in Video AI
In video technology, multimodal AI often deals with the following modalities:
1. Visual: The actual video frames or images
2. Audio: The soundtrack, including speech, music, and sound effects
3. Text: Subtitles, captions, or associated metadata
4. Temporal: The sequential nature of video frames
5. Contextual: Additional information like user data or environmental sensors

## Multimodal Techniques in Video Processing
Several techniques are crucial for multimodal video processing:
- Cross-modal Learning: Training models to understand relationships between different modalities.
- Fusion Strategies: Methods for combining information from multiple modalities, such as early fusion, late fusion, or hybrid approaches.
- Attention Mechanisms: Techniques that allow the model to focus on relevant parts of each modality.
- Transfer Learning: Applying knowledge gained from one modality to improve performance in another.

## Applications in Video Technology
Multimodal AI has transformed various aspects of video technology:
- Content Understanding: Providing more accurate and nuanced interpretation of video content by considering multiple information sources.
- Enhanced Search and Retrieval: Enabling more precise video search based on combinations of visual, audio, and textual queries.
- Accessibility Features: Improving automatic captioning and audio description by leveraging multiple modalities.
- Video Summarization: Creating more comprehensive and accurate video summaries by considering both visual and audio content.
- Emotion Recognition: Analyzing facial expressions, voice tone, and speech content for more accurate emotion detection in videos.
- Virtual Assistants: Powering more sophisticated AI assistants that can understand and respond to multimodal video-related queries.

## Challenges and Considerations
Multimodal AI in video technology faces a range of challenges, including data alignment to ensure proper synchronization and integration of data from different modalities, scalability issues due to the increased computational requirements of processing multiple data streams, modality imbalance where one modality may be more informative or reliable than others, interpretability concerns around understanding how the model integrates and weighs information from different modalities, and privacy and ethical considerations stemming from the increased potential for invasive analysis when combining multiple data sources.

Overcoming these hurdles will be crucial as multimodal AI continues to evolve and become more widely adopted in video applications.

## The Future of Multimodal AI in Video
As multimodal AI techniques continue to advance, we can expect several exciting developments:
- More Seamless Integration: Improved methods for fusing information from different modalities, leading to more natural and comprehensive video understanding.
- Expansion of Modalities: Incorporation of new types of data, such as biometric or environmental information, into video analysis.
- Personalized Experiences: Multimodal systems that can adapt to individual users' preferences and behaviors across different modalities.
- Advanced Content Creation: AI-powered tools that can generate or edit videos based on multimodal inputs, such as combining text, sketches, and voice commands.

&nbsp;  

Real-time Multimodal Processing: Faster and more efficient algorithms enabling sophisticated multimodal analysis for live video applications.

As multimodal AI continues to evolve, it promises to revolutionize how we create, analyze, and interact with video content, offering richer, more contextual, and more personalized video experiences across various platforms and applications.

## What is an MP4 format video?
MP4, sometimes referred to as MPEG-4, has been around since 2001, making it one of the oldest digital video formats we have. It's a popular video container that's commonly used to store video and audio, but sometimes it's used for other media, including subtitles and images. This versatile video container allows streaming on the internet. 

## How was the MP4 video container created?
The MP4 video container was created in 2001, and is based on the QuickTime video container created by Apple. It was created under the ISO/IEC 14496-12:2001 standard by the joint efforts of the ISO/IEC (International Electrotechnical Commission/International Organization for Standardization). MP4s have wider support across devices than MOV files (they also offer higher compression than MOV files do). MP4 is so prevalent because it offers a great combination of quality, efficient compression, and flexibility -- most streaming protocols support MP4. 

## Video codecs used with MP4

The MP4 is used for many different codecs, including:
* H.264 - which is also called Advanced Video Coding or AVC. It's used for recording, compressing, and distributing video on the internet. It's been in existence since 2003 and is currently the most popular codec in use.
* H.265/HEVC - similar to H.264, the H.265 codec is also known as High-Efficiency Video Coding or HEVC. It's 25-50% more efficient with data compression than H.264, but isn't as popular because you must pay to use it. However, its improved compression efficiency means that big companies like Netflix, Amazon Prime and Hulu routinely use it to stream video. The cost is worth it.
* AV1 - known as AOMedia Video 1, this is an open and royalty free codec that's specifically for streaming video across the internet. It's 30% more efficient than H.265. Most people speculate that this format isn't more popular simply because its still very new compared to the other two codecs mentioned.

## Audio codecs used with MP4

The most common audio codec used with MP4 is [AAC (Advanced Audio Coding)](https://api.video/what-is/aac-advanced-audio-coding/). MP4 supports the following audio codecs:
* AAC - see [AAC (Advanced Audio Coding)](https://api.video/what-is/aac-advanced-audio-coding/)
* MPEG-1 Audio Layer 1 - commonly abbreviated to MP1 this is an audio codec that's part of the MPEG-1 standard. It is for use with applications that don't need audio to be compressed a lot. It uses a simple algorithm that's easier to execute, meaning that less complex hardware can handle it. Today this is considered obsolete.
* MPEG-1 Audio Layer 2 - commonly abbreviated to MP2, this audio codec is also part of the MPEG-1 standard. It's still the dominant standard for audio broadcasting and is known for its ability to provide high quality audio in a monophonic channel (a single channel that combines all sounds). It offers low delay and low complexity, making it great for broadcasting.
* MPEG-1 Audio Layer 3 - known as MP3 this is one of the most popular audio codecs in teh world. It's typically used for music. MP3 compression is efficient, because it throws away components of sound data that humans can't hear. It can reduce original audio files by 75 to 95%. 
* CELP - this stands for code-excited linear prediction and it's for predictive speech coding. It excels at providing high quality coded speech at a very low bit rate of 4.8kbps.
* TwinVQ - this stands for transform domain weighted interleave vector quantization...which is fun to say three times fast. This audio codec is optimzed to send good quality audio at ultra low bitrates of 8 kbits/s. It's part of MPEG-4 audio.
* Vorbis - Vorbis is a patent and royalty free audio codec that anyone can use to compress mid to high quality polyphonic audio and music. 
* Apple Lossless - an audio coding format developed by Apple for lossless data compression of digital music. 

The problem is that many devices do not support all these codecs. For example, Chrome will only support AAC in MP4 containers. 

Typically, if an MP4 contains only audio, that file will have M4A as the extension. 

## MP4 at api.video

Each video uploaded to api.video will have a h264 encoded mp4 file created (unless the [mp4Support parameter](https://docs.api.video/reference#post-video) is set to false.

Glossary-Mp4.svg

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Glossary-Moderation.svg

## What is video moderation?

Video moderation is the process in which videos are vetted for content before being published.  Videos are scanned for content to ensure that the videos published meet the requirements of the company or website.

Moderation is typically done by a team of people assigned to watch each video, and judge the content against allowed criteria.  More recently, machine learning algorithms have arisen to help the moderation process.


## Why do I need video moderation?

Adding User Generated Content (UGC) to your site is becoming a popular way to grow a feeling of community around your content.  UGC is highly trusted - for example,  reviews by fellow consumers are generally highly trusted by viewers.  However, with more and more videos being uploaded, many companies are understandably concerned that a customer could, in bad faith, upload a video with inappropriate content, harming the reputation of the company.  By moderating every video prior to publication, they can ensure that every video meets their standards of publication.

## How can I implement moderation?

If the number of videos being uploaded is small, you can have a person (or a team of people) moderate the videos submitted.  videos that meet the submission guidelines are published, while videos that do not are removed from the pipeline.

There are a growing number of moderation services that utilize machine learning to examine the videos for content - these can be used as a "pre-filter" to protect your moderators from the most egregious content. If you find a high level of trust in the moderation software, it could also be used as a stand-alone moderation (without human moderators).

## Moderation at api.video

Videos uploaded are not moderated or flagged for content by api.video.  If you would like to moderate videos uploaded into your account, we have built a demo showing an example implementation. The sample app is available at [moderate.a.video](https://moderate.a.video).

When a new video is uploaded, it is tagged as "uncategorized."  Videos with this tag are not displayed on the site, but are sent to a moderation API that tests the video for adult content, smoking, weapons and hate groups. When the results are returned to the server, the video is retagged with the findings of the algorithm (either 'safe for work' or 'not safe for work', etc.).  The website then can use these tags to decide which videos are appropriate to show to their customers.



## What are AI Models?

In the context of video and AI technology, models refer to mathematical representations or frameworks designed to process, analyze, or generate video content. These models are typically computer programs that have been trained on large datasets to perform specific tasks related to video processing. They serve as the backbone of various AI-powered video applications, ranging from content analysis to video generation.

## Types of Models in Video AI

Several types of models are commonly used in video AI:

1. Predictive Models: Forecast future frames or events in a video sequence.
2. Classification Models: Categorize videos or elements within videos into predefined classes.
3. Generative Models: Create new video content or modify existing footage.
4. Detection Models: Identify and locate specific objects or features within video frames.
5. Segmentation Models: Divide video frames into meaningful segments or regions.

These models often leverage [neural networks](https://api.video/what-is/neural-networks/) and other [machine learning](https://api.video/what-is/machine-learning/) techniques to achieve their objectives.

## Key Components of Video AI Models

Video AI models are typically composed of several key components that work in concert to process and generate video content. At the core is the model's architecture, which defines the overall structure, such as convolutional layers for spatial analysis or recurrent layers for temporal processing. The model's parameters are the adjustable values that are fine-tuned during the training process to optimize performance. A loss function serves as a measure of how well the model is performing its intended task, while an optimizer algorithm is responsible for adjusting the model's parameters to minimize this loss.

Crucial to the model's operation are the input and output layers, which serve as interfaces for receiving video data and producing the desired results. These interconnected components, each playing a vital role, form the foundation of sophisticated Video AI systems capable of tackling a wide range of video-related tasks and applications.

## Applications in Video Technology

Models play a crucial role in various video technology applications:
- Video Compression: Models can learn efficient representations of video data, enabling more effective compression techniques.
- Content Analysis: AI models can automatically tag, categorize, and describe video content, facilitating easier search and organization.
- Visual Effects: Generative models can create or enhance visual effects, such as style transfer or object removal.
- Video Editing: Models can assist in tasks like automatic color grading, scene detection, or even suggesting edits based on content and style.
- Quality Enhancement: Models can upscale resolution, reduce noise, or correct artifacts in video footage.
- Personalized Content Delivery: Recommendation models can analyze user preferences and video characteristics to suggest relevant content.

## Challenges and Considerations

Developing and implementing models for video AI presents several challenges:
- Computational Complexity: Processing high-resolution video data often requires significant computational resources.
- Data Requirements: Many models need large amounts of diverse, high-quality video data for effective training.
- Real-Time Performance: Achieving low-latency performance for real-time video applications can be challenging, especially on consumer devices.
- Interpretability: Complex models, particularly deep learning models, can be difficult to interpret, raising concerns about transparency and accountability.
- Bias and Fairness: Models can inadvertently learn and perpetuate biases present in their training data, leading to unfair or discriminatory outcomes.

## The Future of Models in Video AI

As technology advances, we can anticipate exciting developments in video AI models. Future models will likely integrate analysis of visual, audio, and textual data more seamlessly, enabling more comprehensive video understanding. Advancements in model compression and efficient architectures will enable more powerful models to run on edge devices. Self-supervised learning approaches could reduce the need for large amounts of labeled data, making it easier to train models for specific video tasks.

Additionally, adaptive models that can dynamically adjust their architecture or parameters based on input video characteristics or task requirements, as well as the development of techniques to make complex video AI models more interpretable and transparent, are promising areas of future progress in this rapidly evolving field.

As models continue to evolve, they will drive innovation in video technology, enabling more sophisticated analysis, editing, and generation capabilities. However, addressing challenges related to efficiency, fairness, and ethical use will be crucial in realizing the full potential of these models in the video industry.

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Muted

What is the muted attribute in HTML5?

Muted with api.video