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Section I Fundamentals
© 2000 by CRC Press LLC
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Introduction
Image and video data compression* refers to a process in which the amount of data used to represent image and video is reduced to meet a bit rate requirement (below or at most equal to the maximum available bit rate), while the quality of the reconstructed image or video satisfies a requirement for a certain application and the complexity of computation involved is affordable for the application. The block diagram in Figure 1.1 shows the functionality of image and video data compression in visual transmission and storage. Image and video data compression has been found to be necessary in these important applications, because the huge amount of data involved in these and other applications usually greatly exceeds the capability of today’s hardware despite rapid advancements in the semiconductor, computer, and other related industries. It is noted that information and data are two closely related yet different concepts. Data represent information, and the quantity of data can be measured. In the context of digital image and video, data are usually measured by the number of binary units (bits). Information is defined as knowledge, facts, and news according to the Cambridge International Dictionary of English. That is, while data are the representations of knowledge, facts, and news, information is the knowledge, facts, and news. Information, however, may also be quantitatively measured. The bit rate (also known as the coding rate), is an important parameter in image and video compression and is often expressed in a unit of bits per second, which is suitable in visual communication. In fact, an example in Section 1.1 concerning videophony (a case of visual transmission) uses the bit rate in terms of bits per second (bits/sec, or simply bps). In the application of image storage, the bit rate is usually expressed in a unit of bits per pixel (bpp). The term pixel is an abbreviation for picture element and is sometimes referred to as pel. In information source coding, the bit rate is sometimes expressed in a unit of bits per symbol. In Section 1.4.2, when discussing noiseless source coding theorem, we consider the bit rate as the average length of codewords in the unit of bits per symbol. The required quality of the reconstructed image and video is application dependent. In medical diagnoses and some scientific measurements, we may need the reconstructed image and video to mirror the original image and video. In other words, only reversible, information-preserving schemes are allowed. This type of compression is referred to as lossless compression. In applications such as motion pictures and television (TV), a certain amount of information loss is allowed. This type of compression is called lossy compression. From its definition, one can see that image and video data compression involves several fundamental concepts including information, data, visual quality of image and video, and computational complexity. This chapter is concerned with several fundamental concepts in image and video compression. First, the necessity as well as the feasibility of image and video data compression are discussed. The discussion includes the utilization of several types of redundancies inherent in image and video data, and the visual perception of the human visual system (HVS). Since the quality of the reconstructed image and video is one of our main concerns, the subjective and objective measures of visual quality are addressed. Then we present some fundamental information theory results, considering that they play a key role in image and video compression.
* In this book, the terms image and video data compression, image and video compression, and image and video coding are synonymous.
© 2000 by CRC Press LLC
FIGURE 1.1 Image and video compression for visu