Chao Yu and Ilker Demirkol ECE Dept, University of ... · Chao Yu and Ilker Demirkol ECE Dept,...
Transcript of Chao Yu and Ilker Demirkol ECE Dept, University of ... · Chao Yu and Ilker Demirkol ECE Dept,...
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Chao Yu and Ilker DemirkolECE Dept, University of Rochester
Rochester NY, USA
3 March 2009
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Lower the bit-rate R by allowing some acceptable distortion D of the signal.
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Rate and Distortion is also affected by Communication
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Sensors are power-limited
Consider power-efficiency in dominant operations
Dominant operations:
Sensing, Computation, Communication
Computation: Source coding
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Source coding Rs , Compression distortion , Compression power Rs , Compression distortion , Compression power
Channel coding Rc , Transmission error , Transmission power Rc , Transmission error , Transmission power
ModulationEb or Es is related to bit error rate (BER) Eb , Transmission error , Transmission power Eb , Transmission error , Transmission power
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Possible Categorization: R-D analysis (Video Coding)
R-D analysis (Communication)
P-R-D analysis (Video Coding)
P-R-D analysis (Video Coding and Communication)
P-D analysis (Communication)
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P-R-D model for a system that
automatically adjust its complexity control parameters ▪ the available energy supply
▪ while maximizing the picture quality.
Using dynamic voltage scaling (DVS), the complexityscalability can be translated into energy consumption scalability
P fCLK3
f C (number of processor cycles/sec)
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P-D and R-D results:
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P-R-D Results:
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P- R (~ Encoding Complexity):
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P-R-D for Encoding+Transmission:
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Results (compared to fixed power):
: microprocessor power consumption parameter
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Channel: 2-state Markov model describing burst errors on the symbol level.
Reed–Solomon codes for forward error correction. Simulation using an H.263 video codec
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FEC rate: r = k/n To maintain a constant channel data rate:
Re = r.Rc
Mother&Daughter Foreman
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Results of numerical minimization of Dd for Mother&Daughter
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Objective:
To minimize distortion, transmission power allocated across packets
Proposes:
Two power allocation algorithms transmission power to packets according to their relative importance
Fixed frame power vs variable frame power▪ Morepower to the packets whose lose would result higher distortion
▪ More power to frames with high motion
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Error detection: CRC, Error correction: Convolutional coding BPSK modulation
Results for two QCIF video sequences:
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Objective:
Improve the visual quality of the regions of interest while saving bits, and also adapting to time-varying wireless channels.
Method:
Segment a frame into ROI and non-ROI.
Then allocate more power as well as more bits to ROI to reduce the packet retransmission rate.
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“The human visual system (HVS) is more sensitive to themoving regions”
Moving regions are classified as the foreground (ROI) while still regions are regarded as the background (non-ROI).
Results: