Institute for Telecommunication Sciences / Resources / Video Quality Research /
Objective Measures of Transmission Quality
Propagation modeling, interference mitigation, and spectrum sharing schemes all speak to ensuring reliable radio channels between nodes so that users may successfully communicate data, speech, video, or other media signals. The radio channel is indeed a critical foundation for countless services and applications. But layers of protocols that facilitate sharing, robustness, encoding, and decoding often mean that users’ perceptions of the communications experience vary significantly from what physical measurements of the condition of the radio channel would lead one to expect.
Objective measures of transmission quality move the measurement paradigm away from the transmission channel and towards the user. Objective measures of transmission quality can provide useful indications of the quality, intelligibility, usability, annoyance, and so forth associated with the user’s experience of an application or service independent of the underlying wired or wireless transmission media and protocols that actually enable the application or service. This measurement approach thus provides critical feedback for system design and provisioning so that users’ expectations, rather than somewhat arbitrary engineering thresholds, can be met.
To provide information that mirrors users’ experiences, these objective measures access media (e.g., speech, audio, image, video) signals or data that would be delivered to the user. Media signals are typically passed through signal processing algorithms that roughly emulate human hearing, vision, judgment, expectations, or other attributes as appropriate. Stable and reliable objective measures that compare received signals with transmitted signals exist for several application areas. ITS works to characterize these, and to develop new measurements that cover important additional emerging application areas.
In general, humans have no problem judging received media signals without seeing or hearing the transmitted media signals for comparison purposes. But for objective measures, the analogous task remains a serious challenge. Algorithms, even those that incorporate intricate and effective perceptual modeling, need that original version for comparison purposes—hence the name “full-reference” or “FR” algorithms. FR algorithms have limited applicability; reliable objective measurements that require only received signals (“no reference” (NR) algorithms) would be much more useful. NR algorithms would open up an entire world of light-weight, in-service, real-time endpoint monitoring, fault detection, and optimization. ITS has long contributed, and continues to contribute, insights and advances towards overcoming the ongoing challenge of achieving a reliable NR algorithm—a goal that is shared by many government, industrial, and academic research teams.