DocumentCode
1998779
Title
Modeling of distortion caused by Markov-model burst packet losses in video transmission
Author
Li, Zhicheng ; Chakareski, Jacob ; Niu, Xiaodun ; Zhang, Yongjun ; Gu, Wanyi
Author_Institution
Key Lab. of Biomed. Inf. & Health Eng., SIAT, China
fYear
2009
fDate
5-7 Oct. 2009
Firstpage
1
Lastpage
6
Abstract
This paper addresses the problem of distortion modeling for video transmission over burst-loss channels characterized by a finite state Markov chain. A distortion trellis model is proposed, enabling us to estimate at the frame level the expected mean-square error (MSE) distortion caused by Markov-model bursty packet losses. A sliding window algorithm is developed to perform the MSE estimation with low complexity. Simulation results show that the proposed models are accurate for all tested average loss rates and average burst lengths. Based on the experimental results, the proposed techniques are used to analyze the impact of average burst length on the average decoded video quality. The proposed model is further extended to a more general form, and the modeled distortion is compared with simulation data. These experiments demonstrate that the extended model is also accurate for all tested loss rates.
Keywords
Markov processes; distortion; finite state machines; mean square error methods; video signal processing; Markov-model burst packet losses; burst-loss channels; distortion modeling; distortion trellis model; finite state Markov chain; mean-square error distortion; sliding window algorithm; video quality; video transmission; Biomedical engineering; Biomedical informatics; Decoding; Internet; Jacobian matrices; Mathematical model; Predictive models; Propagation losses; Testing; Video sequences;
fLanguage
English
Publisher
ieee
Conference_Titel
Multimedia Signal Processing, 2009. MMSP '09. IEEE International Workshop on
Conference_Location
Rio De Janeiro
Print_ISBN
978-1-4244-4463-2
Electronic_ISBN
978-1-4244-4464-9
Type
conf
DOI
10.1109/MMSP.2009.5293345
Filename
5293345
Link To Document