Content-aware scalability-type selection for rate adaptation of scalable video
EURASIP Journal on Applied Signal Processing
SVC adaptation: Standard tools and supporting methods
Image Communication
Content classification-based and QoE-driven video send bitrate adaptation scheme
Proceedings of the 5th International ICST Mobile Multimedia Communications Conference
Scheduling algorithms for broadcasting media with multiple distortion measures
IEEE Transactions on Wireless Communications
Subjective video quality assessment applied to scalable video coding and transmission instability
Proceedings of the 2010 ACM Symposium on Applied Computing
User-Centered video quality assessment for scalable video coding of H.264/AVC standard
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Semantic scalability using tennis videos as examples
Multimedia Tools and Applications
Quality analysis of scalable video coding on unstable transmissions
Multimedia Tools and Applications
QAVA: quota aware video adaptation
Proceedings of the 8th international conference on Emerging networking experiments and technologies
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Scalable video coding offers a flexible representation for video adaptation in multiple dimensions comprising spatial detail and temporal resolution, thus providing great benefits for universal media access (UMA) applications. However, currently most of the approaches address the multidimensional adaptation (MDA) problem in an ad hoc manner. One challenging issue affecting the systematic MDA solution is the difficulty in constructing analytical models in theoretical optimization that capture the relations between video utility and MDA operations. In this paper, we propose a general classification-based prediction framework for selecting the preferred MDA operations based on subjective quality evaluation. For this purpose, we first apply domain-specific knowledge or general unsupervised clustering to construct distinct categories within which the videos share similar preferred MDA operations. Thereafter, a machine learning based method is applied where the low level content features extracted from the compressed video streams are employed to train a framework for the problem of joint signal-to-noise ratio (SNR)-temporal adaptation selection based on the motion compensated three-dimensional subband coding (MC-3DSBC) system. We conduct extensive subjective tests involving 31 subjects, 128 video clips, and formal subjective quality metrics. Statistical analysis of the experimental results confirms the excellent accuracy in using domain knowledge and content features to predict the MDA operation.