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Some people in the industry have a misconception that LZ coding techniques are applied in text compression only and they do not work for compressing any other multimedia data type. In lossless compression mode, the LZ coding techniques have been found to be effective to compress different kinds of images. For example, the popular image-compression algorithm GIF (Graphical Interchange Format) is an implementation of the LZW algorithm and very similar to the Unix compress utility. GIF is effective to compress computer-generated graphical images and pseudocolor or color-mapped images. TIFF (Tag Image File Format) is another industry standard. Some of the modes in TIFF have been developed based on LZ coding. This is useful for compressing dithered binary images, which simulate grayscale images
20 INTRODUCTION TO DATA COMPRESSION
through a variation in the density of black dots. The TIFF Revision 6.0 was released in 1992 and supports numerous data compression schemes such as LZW, CCITT Group 3 and Group 4, and JPEG.
In this chapter, we introduced readers with the fundamentals of data and image compression. We discussed why data compression is important and how it became an integrated part of today’s multimedia computing and communications systems. We discussed some fundamentals including information theory such as discrete memoryless model, entropy, noiseless source coding theorem, unique decipherability, etc., in order to aid the readers to understand the principles behind data compression. We discussed the concepts of classification of compression techniques, performance measures, etc. We also presented brief introduction of various international standards for digital compression techniques of various multimedia data types—image, video, text, audio, data, etc. Different source coding algorithms for data compression, the principles of image compression techniques, and details of JPEG and JPEG2000 image compression standards will be discussed in the following chapters.
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