Open AccessAdaptive Correlation Noise Model for DC Coefficients in Wyner‐Ziv Video Coding
Author(s) -
Qin Hao,
Song Bin,
Zhao Yue,
Liu Haihua
Publication year - 2012
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.12.0111.0273
Subject(s) - algorithm , encoder , coding (social sciences) , mathematics , gaussian , noise (video) , computer science , artificial intelligence , statistics , image (mathematics) , physics , quantum mechanics
An adaptive correlation noise model (CNM) construction algorithm is proposed in this paper to increase the efficiency of parity bits for correcting errors of the side information in transform domain Wyner‐Ziv (WZ) video coding. The proposed algorithm introduces two techniques to improve the accuracy of the CNM. First, it calculates the mean of direct current (DC) coefficients of the original WZ frame at the encoder and uses it to assist the decoder to calculate the CNM parameters. Second, by considering the statistical property of the transform domain correlation noise and the motion characteristic of the frame, the algorithm adaptively models the DC coefficients of the correlation noise with the Gaussian distribution for the low motion frames and the Laplacian distribution for the high motion frames, respectively. With these techniques, the proposed algorithm is able to make a more accurate approximation to the real distribution of the correlation noise at the expense of a very slight increment to the coding complexity. The simulation results show that the proposed algorithm can improve the average peak signal‐to‐noise ratio of the decoded WZ frames by 0.5 dB to 1.5 dB.