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 inSignal processing. Image communication Vol. 25; no. 10; pp. 745 - 760
Main Authors DIKICI, Cağatay, BOZMA, H. Işil
Format Journal Article
LanguageEnglish
Published Amsterdam 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.
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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crossref_primary_10_1109_TCSVT_2015_2389491
crossref_primary_10_1109_TBC_2020_3028322
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
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10.1016/S1047-3203(03)00037-3
10.1109/MSP.2006.1657820
10.1016/j.image.2005.11.007
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Issue 10
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
Language English
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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
URI https://dx.doi.org/10.1016/j.image.2010.08.002
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