In this section, we ll examine the compression challenge and why algorithms are crucial to effective access to image and video information under these constraining factors. During normal video playback, data rates are often in excess of 200 million bits per second, exceeding bandwidth constraints for most computer users, and even many computers. To achieve a reasonable image size and frame rate, compression becomes a necessity for efficient transmission of critical video information. The compression challenge is to achieve the highest-quality image or video given the constraints of both bandwidth, speed and channel reliability. Quality relates to how closely the image or video matches our perception, or expectation, of the real image.
Quality is dependent upon image size, and complexity and the fidelity of the original content.
For video, other factors include frame rate, types of motion, and the number of objects moving in different directions. A high-quality reconstruction should appear almost identical to the original and preserve key features in an expected order. For example, very important features should present a higher quality than just important features. This ensures that information necessary to the perception of quality is preserved and ordered in terms of quality importance during the compression and communication process. Summus stresses that quality should be determined by the ability to reliably deliver the information of interest, not necessarily all possible information.