Video data is a rich source of information, combining moving pictures with sound. Think of it like a sophisticated digital flipbook that also has its own perfectly timed soundtrack. AI systems need to understand individual data types. For video, this involves grasping both its visual and auditory elements and, significantly, how these elements are interwoven over time.
The Visual Component: A Stream of Images (Frames)
The visual part of a video is a rapid succession of still images. Each of these individual still images is called a frame.
- Frames as Images: Every single frame is an image, much like the static images we've discussed. It can be represented as a grid of pixels, where each pixel has color and intensity values. For instance, an HD video frame might have 1920×1080 pixels.
- Frame Rate (FPS): Videos display these frames at a consistent speed, known as the frame rate. This is measured in frames per second (FPS). You might have encountered common frame rates such as 24 FPS for movies, or 30 FPS and 60 FPS for television and online videos. A higher FPS generally leads to smoother perceived motion.
- Data Volume: The frame rate directly impacts the amount of data. For example, a short 10-second video clip recorded at 30 FPS consists of:
10 seconds×30 frames/second=300 individual image frames
For an AI system, processing a video means analyzing hundreds or even thousands of these image frames, often in sequence.
The Auditory Component: Synchronized Sound
Most videos are not silent films. They come with an audio track that carries speech, music, sound effects, or ambient sounds.
- Digital Audio: This audio component is a digital sound signal. As you'll learn (or have learned) in the context of audio data representation, sound waves are converted into a series of numerical samples.
- Synchronization is Important: A defining characteristic of video is the synchronization between the audio and the visual frames. The sound of a person speaking should match their lip movements on screen; the sound of an explosion should occur when you see it. If the audio and video are out of sync, the experience becomes jarring and difficult to understand. This temporal alignment is very important for AI models trying to make sense of video content.
How Video Data is Structured for AI
When an AI model is tasked with understanding video, it must effectively process these two synchronized streams of information. Typically, the video data is first broken down:
- Frame Extraction: The video is deconstructed into its sequence of individual image frames. Each frame can then be processed, perhaps initially like any other static image, to identify objects or features.
- Audio Extraction: The audio track is separated from the visual stream. This audio can then be analyzed using techniques specific to sound processing, such as converting it into a spectrogram to visualize its frequency content over time.
The temporal dimension of video is a major factor. What happens in one frame is usually highly dependent on previous frames and influences subsequent ones. The AI model must address:
- Spatial Information: The content within each individual frame (e.g., objects, scenery, people).
- Temporal Information: How the content changes or moves from one frame to the next over time (e.g., a person walking, a car driving).
- Auditory Information: The meaning conveyed by the sound (e.g., speech, distinct sounds).
- Cross-Modal Relationships: How the visual events and the sounds relate to each other at any given moment.
Here's a diagram illustrating the basic structure of video data:
Video data combines a visual track, composed of a sequence of image frames progressing over time, with an audio track containing sound information that is synchronized with those frames.
Why This Structure Matters for Multimodal AI
Understanding how video data is put together is a necessary first step. When we ask multimodal AI systems to work with video, perhaps to describe what's happening, answer questions about its content, or even generate new video clips, the AI must be able to process and integrate information from both the sequence of images and the associated audio. The ways individual images and audio signals are represented, which are covered in this chapter, provide the building blocks. Later, we will see how AI models learn to handle the temporal aspects and combine these synchronized modalities to perform more complex tasks.
