Input Scene Dependencies:
The advent of digital video compression, storage, and transmission systems has exposed fundamental limitations of techniques and methodologies that have traditionally been used to measure video performance. Traditional performance parameters have relied on the "constancy" of a video system's performance for different input scenes. Thus, one could inject a test pattern or test signal (e.g., a static multi-burst), measure some resulting system attribute (e.g., frequency response), and be relatively confident that the system would respond similarly for other video material (e.g., video with motion). A great deal of research has been performed to relate the traditional analog video performance parameters (e.g., differential gain, differential phase, short time waveform distortion, etc.) to perceived changes in video quality. While the recent advent of video compression, storage, and transmission systems has not invalidated these traditional parameters, it has certainly made their connection with perceived video quality much more tenuous. Digital video systems adapt and change their behavior depending upon the input scene. Therefore, attempts to use input scenes that are different from what is actually used in-service can result in erroneous and misleading results. Variations in subjective performance ratings as large as 3 quality units on a subjective quality scale that runs from 1 to 5 (1=lowest rating, 5=highest rating) have been noted in tests of commercially available systems. While quality dependencies on the input scene tend to become much more prevalent at higher compression ratios, they also are observed at lower compression ratios. For example, subjective test results of 45-Mb/s contribution quality systems (i.e., systems now used by broadcasters to transmit over long-line digital networks) revealed one transmission system with multiple tandem codecs whose subjective performance varied from 2.16 to 4.64 quality units.
A digital video transmission system that works fine for video teleconferencing might be inadequate for entertainment television. Specifying the performance of a digital video system as a function of the video scene coding difficulty yields a much more complete description of system performance. Recognizing the need to select appropriate input scenes for testing, algorithms have been developed for quantifying the expected coding difficulty of an input scene based on the amount of spatial detail and motion. Other methods have been proposed for determining the picture-content failure characteristic for the system under consideration. National and international standards have been developed that specify standard video scenes for testing digital video systems. Use of these standards assures that users compare apples to apples when evaluating similar systems from different suppliers.
Digital Transmission System Dependencies:
The operating characteristics of digital transmission systems (e.g., bit-rate, error rate, dropped packet rate) may change over time and this can produce quality fluctuations. These transients may be very difficult to capture unless the performance of the system is being continuously monitored. Ideally, this monitoring should be done in-service, since taking the transmission system out-of-service and injecting known test signals and/or scenes will change the conditions under which the the digital transmission system is operating, and hence unduly influence the performance measurement. Two examples that demonstrate this effect is the statistical multiplexer, a device which multiplexes variable bit-rate compression of many individual video channels into a single constant bit-rate channel, and digital video transmission over the Internet using non-guaranteed bandwidth. Only continuous, non-intrusive, in-service performance monitoring can accurately capture what the viewer is perceiving in these instances. A new measurement paradigm is thus required.
New Digital Video Impairments:
Digital video systems produce fundamentally different kinds of impairments than analog video systems. Examples of these include tiling, error blocks, smearing, jerkiness, edge busyness, and object retention. To fully quantify the performance characteristics of a digital video system, it is desirable to have a set of performance parameters, where each parameter is sensitive to some unique dimension of video quality or impairment type. This is similar to what was developed for analog impairments (e.g., a multi-burst test would measure the frequency response, and a signal-to-noise ratio test would measure the analog noise level). This discrimination property of performance parameters is useful to designers trying to optimize certain system attributes over others, and to network operators wanting to know not only when a system is failing but where and how it is failing.
Also of interest is how a user weighs the different performance attributes of a digital video system (e.g., spatial resolution, temporal resolution, or color reproduction accuracy) when subjectively rating the quality of the experience. The process of estimating these subjective quality ratings from objective performance parameter data is an important new area of work.
The Need for Technology Independence:
The constancy of analog video systems over the past 4 decades provided the necessary long term development cycle to produce today's accurate analog video test equipment. In contrast, the rapid evolution of digital video compression, storage, and transmission technology presents a much more difficult performance measurement task. To avoid immediate obsolescence, new performance measurement technology developed for digital video systems must be technology independent, or not dependent upon specific coding algorithms or transport architectures. One way to achieve technology independence is to have the test instrument perceive and measure video impairments like a human being. Fortunately, the computational resources needed to achieve these measurement operations are becoming available.
Input Scene Dependencies: