A Novel Rate and Distortion Estimation Method using Particle Filtering based Prediction for Intra-Predictive Coding of Deep Block Partitioning Structures

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In this paper, we propose a new R/D estimation method for intra-predictive coding with deep block partitioning structures. In our proposed R/D prediction, we adopt a particle filtering based prediction (PFP) to precisely predict intermediate R/D estimates for the next frame in a stochastic manner, which helps increasing the prediction accuracy of fast changing R/D values. Then, based on the intermediate R/D estimates by PFP, we infer an optimal model parameter of the TC’s probability density function (pdf) via convex optimization. We found that the proposed method brings about more stable R/D estimation performance thanks to both the improved prediction accuracy using the PFP for abrupt changes in true R/D values and the precise estimation of the optimal model parameter. Experimental results show that our method significantly reduces the normalized root mean square error from average 3.17 to 0.79 (74.90% reduction) for rate and from average 2.32 to 0.82 (64.61% reduction) for distortion, compared to the state-of-the art method.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2021-02
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, v.31, no.2, pp.782 - 797

ISSN
1051-8215
DOI
10.1109/TCSVT.2020.2989185
URI
http://hdl.handle.net/10203/281098
Appears in Collection
EE-Journal Papers(저널논문)
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