TY - GEN
T1 - How to Design a R-D Model for UAV Video Transmission?
AU - Yu, Zongyang
AU - Yang, Peng
AU - Cao, Xianbin
AU - Zheng, Dezhi
AU - Quek, Tony Q.S.
AU - Oliver Wu, Dapeng
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Unmanned aerial vehicle (UAV) video transmission has been extensively applied in many crucial fields. However, the problem of designing a rate-distortion (R-D) model for UAV video transmission, which is essential for theoretical analysis of video coding and optimization of video transmission quality, is under-studied. The designed R-D model is desired to be simple, accurate, and generic owing to the limited capability of the UAV. Observing the key role of transformed residuals in the R-D model, this paper elaborately discusses the modeling of the transformed residual distribution and proposes an estimation algorithm to estimate the statistical parameter of the distribution. Specifically, considering the stringent requirements for low complexity and high generalization capability, we first design a linear parameter estimator by mining and analyzing UAV video statistical characteristics. Further, we develop a bias update scheme to improve the accuracy of the estimator. Test results on multiple real video sequences taken by UAVs show that the proposed estimation algorithm is more accurate than benchmarks.
AB - Unmanned aerial vehicle (UAV) video transmission has been extensively applied in many crucial fields. However, the problem of designing a rate-distortion (R-D) model for UAV video transmission, which is essential for theoretical analysis of video coding and optimization of video transmission quality, is under-studied. The designed R-D model is desired to be simple, accurate, and generic owing to the limited capability of the UAV. Observing the key role of transformed residuals in the R-D model, this paper elaborately discusses the modeling of the transformed residual distribution and proposes an estimation algorithm to estimate the statistical parameter of the distribution. Specifically, considering the stringent requirements for low complexity and high generalization capability, we first design a linear parameter estimator by mining and analyzing UAV video statistical characteristics. Further, we develop a bias update scheme to improve the accuracy of the estimator. Test results on multiple real video sequences taken by UAVs show that the proposed estimation algorithm is more accurate than benchmarks.
UR - http://www.scopus.com/inward/record.url?scp=85185875765&partnerID=8YFLogxK
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U2 - 10.1109/WCSP58612.2023.10404357
DO - 10.1109/WCSP58612.2023.10404357
M3 - Conference contribution
AN - SCOPUS:85185875765
T3 - 2023 IEEE 15th International Conference on Wireless Communications and Signal Processing, WCSP 2023
SP - 31
EP - 36
BT - 2023 IEEE 15th International Conference on Wireless Communications and Signal Processing, WCSP 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023
Y2 - 2 November 2023 through 4 November 2023
ER -