QoS-message-directed adaptive multimedia communication

From the 1994 CAIS Conference: The Information Industry in Transition McGill University, Montreal, Quebec. May 25 - 27, 1994.Broadband integrated services digital network (B-ISDN) based on the asynchronous transmission mode (ATM) is becoming reality to provide high speed, multi bit rate multimedia communications. Multimedia communication network has to support voice, video and data traffics that have different traffic characteristics, delay sensitive or loss sensitive features have to be accounted for designing high speed multimedia information networks. In this paper, we formulate the network design problem by considering the multimedia communication requirements. A high speed multimedia information network design alogrithm is developed using a stochastic optimization method to find good solutions which meet the Quality of Service (QoS) requirement of each traffic class with minimum cost.

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Digital TV and Wireless Multimedia Communication

10.1007/978-981-16-1194-0 ◽

2021 ◽

Keyword(s):

Multimedia Communication ◽

Digital Tv ◽

Wireless Multimedia

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Assessment of Machine Learning-Based Audiovisual Quality Predictors

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3430376 ◽

2021 ◽

Vol 17 (2) ◽

pp. 1-22

Author(s):

Mythili K. ◽

Manish Narwaria

Keyword(s):

Machine Learning ◽

Signal Processing ◽

Quality Assessment ◽

Computational Models ◽

Model Development ◽

Multimedia Communication ◽

Current Approach ◽

Learning Ability ◽

Data Uncertainty ◽

Attractive Alternative

Quality assessment of audiovisual (AV) signals is important from the perspective of system design, optimization, and management of a modern multimedia communication system. However, automatic prediction of AV quality via the use of computational models remains challenging. In this context, machine learning (ML) appears to be an attractive alternative to the traditional approaches. This is especially when such assessment needs to be made in no-reference (i.e., the original signal is unavailable) fashion. While development of ML-based quality predictors is desirable, we argue that proper assessment and validation of such predictors is also crucial before they can be deployed in practice. To this end, we raise some fundamental questions about the current approach of ML-based model development for AV quality assessment and signal processing for multimedia communication in general. We also identify specific limitations associated with the current validation strategy which have implications on analysis and comparison of ML-based quality predictors. These include a lack of consideration of: (a) data uncertainty, (b) domain knowledge, (c) explicit learning ability of the trained model, and (d) interpretability of the resultant model. Therefore, the primary goal of this article is to shed some light into mentioned factors. Our analysis and proposed recommendations are of particular importance in the light of significant interests in ML methods for multimedia signal processing (specifically in cases where human-labeled data is used), and a lack of discussion of mentioned issues in existing literature.

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