The following sections describe various types of data that you might find, in addition to static graphics data, in multimedia files.
Somewhere between the motionless world of still images and the real-time world of video images lies the flip-book world of computer animation. All of the animated sequences seen in educational programs, motion CAD renderings, and computer games are computer-animated (and in many cases, computer-generated) animation sequences.
Traditional cartoon animation is little more than a series of artwork cells, each containing a slight positional variation of the animated subjects. When a large number of these cells is displayed in sequence and at a fast rate, the animated figures appear to the human eye to move.
A computer-animated sequence works in exactly the same manner. A series of images is created of a subject; each image contains a slightly different perspective on the animated subject. When these images are displayed (played back) in the proper sequence and at the proper speed (frame rate), the subject appears to move.
Computerized animation is actually a combination of both still and motion imaging. Each frame, or cell, of an animation is a still image that requires compression and storage. An animation file, however, must store the data for hundreds or thousands of animation frames and must also provide the information necessary to play back the frames using the proper display mode and frame rate.
Animation file formats are only capable of storing still images and not actual video information. It is possible, however, for most multimedia formats to contain animation information, because animation is actually a much easier type of data than video to store.
The image-compression schemes used in animation files are also usually much simpler than most of those used in video compression. Most animation files use a delta compression scheme, which is a form of Run-Length Encoding that stores and compresses only the information that is different between two images (rather than compressing each image frame entirely). RLE is relatively easy to decompress on the fly. (See Chapter 9, Data Compression, for a description of RLE compression.)
Storing animations using a multimedia format also produces the benefit of adding sound to the animation (what's a cartoon without sound?). Most animation formats cannot store sound directly in their files and must rely on storing the sound in a separate disk file which is read by the application that is playing back the animation.
Animations are not only for entertaining kids and adults. Animated sequences are used by CAD programmers to rotate 3D objects so they can be observed from different perspectives; mathematical data collected by an aircraft or satellite may be rendered into an animated fly-by sequence. Movie special effects benefit greatly by computer animation.
For more specific information on animation, refer to the articles on the FLI and GRASP animation formats.
One step beyond animation is broadcast video. Your television and video tape recorder are a lot more complex than an 8mm home movie projector and your kitchen wall. There are many complex signals and complicated standards that are involved in transmitting those late-night reruns across the airwaves and cable. Only in the last few years has a personal computer been able to work with video data at all.
Video data normally occurs as continuous, analog signals. In order for a computer to process this video data, we must convert the analog signals to a non-continuous, digital format. In a digital format, the video data can be stored as a series of bits on a hard disk or in computer memory.
The process of converting a video signal to a digital bitstream is called analog-to-digital conversion (A/D conversion), or digitizing. A/D conversion occurs in two steps:
Each sample captured from the video stream is typically stored as a 16-bit integer. The rate at which samples are collected is called the sampling rate. The sampling rate is measured in the number of samples captured per second (samples/second). For digital video, it is necessary to capture millions of samples per second.
Quantizing converts the level of a video signal sample into a discrete, binary value. This value approximates the level of the original video signal sample. The value is selected by comparing the video sample to a series of predefined threshold values. The value of the threshold closest to the amplitude of the sampled signal is used as the digital value.
A video signal contains several different components which are mixed together in the same signal. This type of signal is called a composite video signal and is not really useful in high-quality computer video. Therefore, a standard composite video signal is usually separated into its basic components before it is digitized.
The composite video signal format defined by the NTSC (National Television Standards Committee) color television system is used in the United States. The PAL (Phase Alternation Line) and SECAM (Sequential Coleur Avec Memoire) color television systems are used in Europe and are not compatible with NTSC. Most computer video equipment supports one or more of these system standards.
The components of a composite video signal are normally decoded into three separate signals representing the three channels of a color space model, such as RGB, YUV, or YIQ. Although the RGB model is quite commonly used in still imaging, the YUV, YIQ, or YCbCr models are more often used in motion-video imaging. TV practice uses YUV or similar color models because the U and V channels can be downsampled to reduce data volume without materially degrading image quality.
The three composite channels mentioned here are the same channels used in the downsampling stage of JPEG compression; for more information, see the section called "JPEG Compression" of Chapter 9.
Once the video signal is converted to a digital format, the resulting values can be represented on a display device as pixels. Each pixel is a spot of color on the video display, and the pixels are arranged in rows and columns just as in a bitmap. Unlike a static bitmap, however, the pixels in a video image are constantly being updated for changes in intensity and color. This updating is called scanning, and it occurs 60 times per second in NTSC video signals (50 times per second for PAL and SECAM).
A video sequence is displayed as a series of frames. Each frame is a snapshot of a moment in time of the motion-video data, and is very similar to a still image. When the frames are played back in sequence on a display device, a rendering of the original video data is created. In real-time video the playback rate is 30 frames per second. This is the minimum rate necessary for the human eye to successfully blend each video frame together into a continuous, smoothly moving image.
A single frame of video data can be quite large in size. A video frame with a resolution of 512 x 482 will contain 246,784 pixels. If each pixel contains 24 bits of color information, the frame will require 740,352 bytes of memory or disk space to store. Assuming there are 30 frames per second for real-time video, a 10-second video sequence would be more than 222 megabytes in size! It is clear there can be no computer video without at least one efficient method of video data compression.
There are many encoding methods available that will compress video data. The majority of these methods involve the use of a transform coding scheme, usually employing a Fourier or Discrete Cosine Transform (DCT). These transforms physically reduce the size of the video data by selectively throwing away unneeded parts of the digitized information. Transform compression schemes usually discard 10 percent to 25 percent or more of the original video data, depending largely on the content of the video data and upon what image quality is considered acceptable.
Usually a transform is performed on an individual video frame. The transform itself does not produce compressed data. It discards only data not used by the human eye. The transformed data, called coefficients, must have compression applied to reduce the size of the data even further. Each frame of data may be compressed using a Huffman or arithmetic encoding algorithm, or even a more complex compression scheme such as JPEG. (See Chapter 9 for a discussion of these compression methods.) This type of intraframe encoding usually results in compression ratios between 20:1 to 40:1 depending on the data in the frame. However, even higher compression ratios may result if, rather than looking at single frames as if they were still images, we look at multiple frames as temporal images.
In a typical video sequence, very little data changes from frame to frame. If we encode only the pixels that change between frames, the amount of data required to store a single video frame drops significantly. This type of compression is known as interframe delta compression, or in the case of video, motion compensation. Typical motion compensation schemes that encode only frame deltas (data that has changed between frames) can, depending on the data, achieve compression ratios upwards of 200:1.
This is only one possible type of video compression method. There are many other types of video compression schemes, some of which are similar and some of which are different. For more information on compression methods, refer to Chapter 9 and to the articles in Part Two that describe multimedia file formats.
All multimedia file formats are capable, by definition, of storing sound information. Sound data, like graphics and video data, has its own special requirements when it is being read, written, interpreted, and compressed. Before looking at how sound is stored in a multimedia format we must look at how sound itself is stored as digital data.
All of the sounds that we hear occur in the form of analog signals. An analog audio recording system, such as a conventional tape recorder, captures the entire sound wave form and stores it in analog format on a medium such as magnetic tape.
Because computers are now digital devices it is necessary to store sound information in a digitized format that computers can readily use. A digital audio recording system does not record the entire wave form as analog systems do (the exception being Digital Audio Tape [DAT] systems). Instead, a digital recorder captures a wave form at specific intervals, called the sampling rate. Each captured wave-form snapshot is converted to a binary integer value and is then stored on magnetic tape or disk.
Storing audio as digital samples is known as Pulse Code Modulation (PCM). PCM is a simple quantizing or digitizing (audio to digital conversion) algorithm, which linearly converts all analog signals to digital samples. This process is commonly used on all audio CD-ROMs.
Differential Pulse Code Modulation (DPCM) is an audio encoding scheme that quantizes the difference between samples rather than the samples themselves. Because the differences are easily represented by values smaller than those of the samples themselves, fewer bits may be used to encode the same sound (for example, the difference between two 16-bit samples may only be four bits in size). For this reason, DPCM is also considered an audio compression scheme.
One other audio compression scheme, which uses difference quantization, is Adaptive Differential Pulse Code Modulation (ADPCM). DPCM is a non-adaptive algorithm. That is, it does not change the way it encodes data based on the content of the data. DPCM uses the sample number of bits to represent every signal level. ADPCM, however, is an adaptive algorithm and changes its encoding scheme based on the data it is encoding. ADPCM specifically adapts by using fewer bits to represent lower-level signals than it does to represent higher-level signals. Many of the most commonly used audio compression schemes are based on ADPCM.
Digital audio data is simply a binary representation of a sound. This data can be written to a binary file using an audio file format for permanent storage much in the same way bitmap data is preserved in an image file format. The data can be read by a software application, can be sent as data to a hardware device, and can even be stored as a CD-ROM.
The quality of an audio sample is determined by comparing it to the original sound from which it was sampled. The more identical the sample is to the original sound, the higher the quality of the sample. This is similar to comparing an image to the original document or photograph from which it was scanned.
The quality of audio data is determined by three parameters:
The sample resolution is determined by the number of bits per sample. The larger the sampling size, the higher the quality of the sample. Just as the apparent quality (resolution) of an image is reduced by storing fewer bits of data per pixel, so is the quality of a digital audio recording reduced by storing fewer bits per sample. Typical sampling sizes are eight bits and 16 bits.
The sampling rate is the number of times per second the analog wave form was read to collect data. The higher the sampling rate, the greater the quality of the audio. A high sampling rate collects more data per second than a lower sampling rate, therefore requiring more memory and disk space to store. Common sampling rates are 44.100 kHz (higher quality), 22.254 kHz (medium quality), and 11.025 kHz (lower quality). Sampling rates are usually measured in the signal processing terms hertz (Hz) or kilohertz (kHz), but the term samples per second (samples/second) is more appropriate for this type of measurement.
A sound source may be sampled using one channel (monaural sampling) or two channels (stereo sampling). Two-channel sampling provides greater quality than mono sampling and, as you might have guessed, produces twice as much data by doubling the number of samples captured. Sampling one channel for one second at 11,000 samples/second produces 11,000 samples. Sampling two channels at the same rate, however, produces 22,000 samples/second.
The amount of binary data produced by sampling even a few seconds of audio is quite large. Ten seconds of data sampled at low quality (one channel, 8-bit sample resolution, 11.025 samples/second sampling rate) produces about 108K of data (88.2 Kbits/second). Adding a second channel doubles the amount of data to produce nearly a 215K file (176 Kbits/second). If we increase the sample resolution to 16 bits, the size of the data doubles again to 430K (352 Kbits/second). If we now increase the sampling rate to 22.05 Ksamples/second, the amount of data produced doubles again to 860K (705.6 Kbits/second). At the highest quality generally used (two channels, 16-bit sample resolution, 44.1 Ksamples/second sampling rate), our 10 seconds of audio now requires 1.72 megabytes (1411.2 Kbits/second) of disk space to store.
Consider how little information can really be stored in 10 seconds of sound. The typical musical song is at least three minutes in length. Music videos are from five to 15 minutes in length. A typical television program is 30 to 60 minutes in length. Movie videos can be three hours or more in length. We're talking a lot of disk space here.
One solution to the massive storage requirements of high-quality audio data is data compression. For example, the CD-DA (Compact Disc-Digital Audio) standard performs mono or stereo sampling using a sample resolution of 16 bits and a sampling rate of 44.1 samples/second, making it a very high-quality format for both music and language applications. Storing five minutes of CD-DA information requires approximately 25 megabytes of disk space--only half the amount of space that would be required if the audio data were uncompressed.
Audio data, in common with most binary data, contains a fair amount of redundancy that can be removed with data compression. Conventional compression methods used in many archiving programs (zoo and pkzip, for example) and image file formats don't do a very good job of compressing audio data (typically 10 percent to 20 percent). This is because audio data is organized very differently from either the ASCII or binary data normally handled by these types of algorithms.
Audio compression algorithms, like image compression algorithms, can be categorized as lossy and lossless. Lossless compression methods do not discard any data. The decompression step produces exactly the same data as was read by the compression step. A simple form of lossless audio compression is to Huffman-encode the differences between each successive 8-bit sample. Huffman encoding is a lossless compression algorithm and, therefore the audio data is preserved in its entirety.
Lossy compression schemes discard data based on the perceptions of the psychoacoustic system of the human brain. Parts of sounds that the ear cannot hear, or the brain does not care about, can be discarded as useless data.
An algorithm must be careful when discarding audio data. The ear is very sensitive to changes in sound. The eye is very forgiving about dropping a video frame here or reducing the number of colors there. The ear, however, notices even slight changes in sounds, especially when specifically trained to recognize audial infidelities and discrepancies. However, the higher the quality of an audio sample, the more data will be required to store it. As with lossy image compression schemes, at times you'll need to make a subjective decision between quality and data size.
There is currently no "audio file interchange format" that is widely used in the computer-audio industry. Such a format would allow a wide variety of audio data to be easily written, read, and transported between different hardware platforms and operating systems.
Most existing audio file formats, however, are very machine-specific and do not lend themselves to interchange very well. Several multimedia formats are capable of encapsulating a wide variety of audio formats, but do not describe any new audio data format in themselves.
Many audio file formats have headers just as image files do. Their header information includes parameters particular to audio data, including sample rate, number of channels, sample resolution, type of compression, and so on. An identification field ("magic" number) is also included in several audio file format headers.
Several formats contain only raw audio data and no file header. Any parameters these formats use are fixed in value and therefore would be redundant to store in a file header. Stream-oriented formats contain packets (chunks) of information embedded at strategic points within the raw audio data itself. Such formats are very platform-dependent and would require an audio file format reader or converter to have prior knowledge of just what these parameter values are.
Most audio file formats may be identified by their file types or extensions. Some common sound file formats are:
| .AU | Sun Microsystems |
| .SND | NeXT |
| HCOM | Apple Macintosh |
| .VOC | SoundBlaster |
| .WAV | Microsoft Waveform |
| AIFF | Apple/SGI |
| 8SVX | Apple/SGI |
A multimedia format may choose to either define its own internal audio data format or simply encapsulate an existing audio file format. Microsoft Waveform files are RIFF files with a single Waveform audio file component, while Apple QuickTime files contain their own audio data structures unique to QuickTime files. For further information about audio, see the section called "Audio Formats" in Chapter 1, Introduction.
Musical Instrument Digital Interface (MIDI) is an industry standard for representing sound in a binary format. MIDI is not an audio format, however. It does not store actual digitally sampled sounds. Instead, MIDI stores a description of sounds, in much the same way that a vector image format stores a description of an image and not image data itself.
Sound in MIDI data is stored as a series of control messages. Each message describes a sound event using terms such as pitch, duration, and volume. When these control messages are sent to a MIDI-compatible device (the MIDI standard also defines the interconnecting hardware used by MIDI devices and the communications protocol used to interchange the control information) the information in the message is interpreted and reproduced by the device.
MIDI data may be compressed, just like any other binary data, and does not require special compression algorithms in the way that audio data does.
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