Why Compression?

Despite the many advantages of a digital representation of signals compared to the analog counterpart, they need a very large number of bits for storage and transmission. For example, a high-quality audio signal requires approximately 1.5 megabits per second for digital representation and storage. A television-quality low-resolution color video of 30 frames per second with each frame containing 640 x 480 pixels (24 bits per color pixel) needs more than 210 megabits per second of storage. As a result, a digitized one-hour color movie would require approximately 95 gigabytes of storage.

The storage requirement for upcoming high-definition television (HDTV) of resolution 1280 x 720 at 60 frames per second is far greater. A digitized one-hour color movie of HDTV-quality video will require approximately 560 gigabytes of storage. A digitized 14 x 17 square inch radiograph scanned at 70 pm occupies nearly 45 megabytes of storage.

Transmission of these digital signals through limited bandwidth communication channels is even a greater challenge and sometimes impossible in its raw form. Although the cost of storage has decreased drastically over the past decade due to significant advancement in microelectronics and storage technology, the requirement of data storage and data processing applications is growing explosively to outpace this achievement.

Interestingly enough, most of the sensory signals such as still image, video, and voice generally contain significant amounts of superfluous and redundant information in their canonical representation as far as the human perceptual a system is concerned. By human perceptual system, we mean our eyes and ears. For example, the neighboring pixels in the smooth region of a natural an image is very similar and small variation in the values of the neighboring pixels are not noticeable to the human eye.

The consecutive frames in a stationary or slowly changing scene in a video are very similar and redundant. Some audio data beyond the human audible frequency range are useless for all practical purposes. This fact tells us that there are data in audic-visual signals that cannot be perceived by the human perceptual system. We call this perceptual redundancy. In English text files, common words (e.g., “the”) or similar patterns of character strings (e.g., “ze”, “th”) are usually used repeatedly. It is also observed that the characters in a text file occur in a well-documented distribution, with letter e and “space” being the most popular. In numeric data files, we often observe runs of similar numbers or predictable interdependency among the numbers. We have mentioned only a few examples here. There are many such examples of redundancy in digital representation in all sorts of data.

Data compression is the technique to reduce the redundancies in data representation in order to decrease data storage requirements and hence communication costs. Reducing the storage requirement is equivalent to increasing the capacity of the storage medium and hence communication bandwidth. Thus the development of efficient compression techniques will continue to be a design challenge for future communication systems and advanced multimedia applications.

Source: JPEG2000 Standard
for Image Compression
(Tinku Acharya)


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