Image compression aims to produce a new image representation that can be stored and transmitted efficiently. It is a core technology for multimedia processing and has played a key enabling role in many commercial products, such as digital camera and camcorders. It facilitates visual data transmission through the Internet, contributes to the advent of digital broadcast system, and makes possible the storage on VCD and DVD. Despite a continuing increase in capacity, efficient transmission and storage of images still present the utmost challenge in all these systems. Consequently, fast and efficient compression algorithms are in great demand. The basic principle for image compression is to remove any redundancy in image representation. For example, simple graphic images such as icons and line drawings can be represented more efficiently by considering differences among neighbor pixels, as the differences always have lower entropy value than the original images (Shannon, 1948). These kinds of techniques are often referred to as lossless compression. It tries to exploit statistical redundancy in an image so as to provide a concise representation in which the original image can be reconstructed perfectly. However, statistical compression techniques alone cannot provide high compression ratio. To improve image compressibility, lossy compression is often used so that visually important image features are preserved while some fine details are removed or not represented perfectly. This type of compression is often used for natural images where the loss of some details is generally unnoticeable to viewers. This articles deals with image compression. Specifi- cally, it is concern with compression of natural color images because they constitute the most important class of digital image. First, the basic principle and methodology of natural image compression is described. Then, several major natural image compression standards, namely JPEG, JPEG-LS, and JPEG 2000 are discussed.
A common characteristic of most images is that the neighboring pixels are correlated and thus contain redundant information. The main goal of image compression is to reduce or remove this redundancy. In general, two types of redundancy can be identified (Gonzalez & Woods, 2002):
Spatial redundancy: This refers to the correlation between neighboring pixels. This is the only redundancy for grayscale images.
Spectral redundancy: This refers to the correlation between different color planes or spectral bands. This redundancy occurs in color images or multispectral images and exists together with the spatial redundancy.
Image compression techniques aim at reducing the number of bits needed to represent an image by removing the spatial and spectral redundancies as much as possible. The compression is lossless if the redundancy reduction does not result in any loss of information in the original image.
Besides redundancy, an image may also contain visually irrelevant information. The visually irrelevant information refers to information that is not perceived by human observers. Irrelevancy reduction thus aims at removing certain information in the image that is not noticeable by the Human Visual System (HVS). In general, some form of information loss is incurred when irrelevancy reduction is performed (Xiao, Wu, Wei, & Bao, 2005).
A number of standards have been established over the years for natural image compression. JPEG is the most common image file format that is found in existing Internet and multimedia systems (Pennebaker & Mitchell, 1993; Wallace, 1991). JPEG stands for Joint Photographic Experts Group. It is the name of the joint ISO/CCITT committee that created the image compression standard in 1992. There are two compression modes in the JPEG compression standard: lossless and lossy. However, the lossy mode dominates in almost all applications. The JPEG image compression codec has low complexity and is memory efficient. However, its main criticism is the appearance of the blocking artifacts, especially at high compression ratios.
Key Terms in this Chapter
Wavelet Subband: This refers to a group of the wavelet coefficients at certain frequency ranges.
Region of Interest Coding: This means that certain parts of the image (i.e., the interested regions) are encoded with more bits and thus have better quality than other parts of the image.
JPEG 2000: An image compression standard that aims not only to provide an improved compression performance over JPEG, but also to provide new features such as region of interest coding, random access and progressive coding.
Scalability: It refers to a successive quality change by bitstream manipulation. For example, PSNR scalability means the PSNR improves as more bits in the bitstream are decoded.
Significant Wavelet Coefficients: This refers to wavelet coefficients that have large absolute magnitude. Usually, this implies important structural information such as edges in an image.
Transform Coding: This refers to a type of compression in which the image data is first transformed into another domain so that the data becomes uncorrelated in this new domain for further processing.
Ringing Artifacts: This type of artifacts often appears near the edges of an image in which edges are blurred and have oscillating effect.
JPEG: An image compression standard proposed in 1992 by ISO/CCITT committee. It is one of the most common image file format that is found in Internet and consumer products.
Progressive Transmission: This implies that the bitstream is arranged so that most important information is near the front end of the bitstream and the least important information is at the back of the bitstream. Thus, in decoding, the quality of the decoded image is progressively increased.
Blocking Artifacts: This is one of the artifacts often exhibited by JPEG standard at high compression ratios. Images appear to have regular block structures.