Attention-based video streaming
This paper considers the problem of video streaming in low bandwidth networks and presents a complete framework that is inspired by the fovea–periphery distinction of biological vision systems. First, an application specific attention function that serves to find the important small regions in a giv...
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Published in | Signal processing. Image communication Vol. 25; no. 10; pp. 745 - 760 |
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Main Authors | , |
Format | Journal Article |
Language | English |
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Elsevier B.V
01.11.2010
Elsevier |
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Abstract | This paper considers the problem of video streaming in low bandwidth networks and presents a complete framework that is inspired by the fovea–periphery distinction of biological vision systems. First, an application specific attention function that serves to find the important small regions in a given frame is constructed a priori using a back-propagation neural network that is optimized combinatorially. Given a specific application, the respective attention function partitions each frame into foveal and periphery regions and then a spatial–temporal pre-processing algorithm encodes the foveal regions with high spatial resolution while the periphery regions are encoded with lower spatial and temporal resolution. Finally, the pre-processed video sequence is streamed using a standard streaming server. As an application, we consider the transmission of human face videos. Our experimental results indicate that even with limited amount of training, the constructed attention function is able to determine the foveal regions which have improved transmission quality while the peripheral regions have an acceptable degradation. |
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AbstractList | This paper considers the problem of video streaming in low bandwidth networks and presents a complete framework that is inspired by the fovea-periphery distinction of biological vision systems. First, an application specific attention function that serves to find the important small regions in a given frame is constructed a priori using a back-propagation neural network that is optimized combinatorially. Given a specific application, the respective attention function partitions each frame into foveal and periphery regions and then a spatial-temporal pre-processing algorithm encodes the foveal regions with high spatial resolution while the periphery regions are encoded with lower spatial and temporal resolution. Finally, the pre-processed video sequence is streamed using a standard streaming server. As an application, we consider the transmission of human face videos. Our experimental results indicate that even with limited amount of training, the constructed attention function is able to determine the foveal regions which have improved transmission quality while the peripheral regions have an acceptable degradation. |
Author | Dikici, Çağatay Işıl Bozma, H. |
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Cites_doi | 10.1016/S0167-8655(02)00333-1 10.1109/JPROC.2004.839619 10.1016/1049-9660(92)90081-D 10.1145/507089.507090 10.1109/TIP.2004.834657 10.1152/jn.1996.76.4.2718 10.1109/TIP.2003.819861 10.1007/978-3-540-30125-7_36 10.1109/TIP.2003.809015 10.1016/S1047-3203(03)00037-3 10.1109/MSP.2006.1657820 10.1016/j.image.2005.11.007 |
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Keywords | Spatio-temporal pre-processing Biologically motivated attentive vision Foveation Video streaming Face tracking Neural networks Biometrics High resolution Video signal Vision system Video signal processing Learning Degradation Backpropagation algorithm Image sequence Streaming Computer vision Face recognition Pattern recognition Neural network Information transmission Temporal logic Time resolution A priori estimation Automatic recognition Spatial resolution |
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Snippet | This paper considers the problem of video streaming in low bandwidth networks and presents a complete framework that is inspired by the fovea–periphery... This paper considers the problem of video streaming in low bandwidth networks and presents a complete framework that is inspired by the fovea-periphery... |
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SubjectTerms | Algorithms Applied sciences Artificial intelligence Biological Biologically motivated attentive vision Computer science; control theory; systems Connectionism. Neural networks Degradation Exact sciences and technology Face tracking Foveation Frames Image processing Information, signal and communications theory Networks Neural networks Partitions Pattern recognition Pattern recognition. Digital image processing. Computational geometry Servers Signal processing Spatio-temporal pre-processing Telecommunications and information theory Video streaming |
Title | Attention-based video streaming |
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