Side information and noise learning for distributed video coding using optical flow and clustering

TY - JOUR

T1 - Side information and noise learning for distributed video coding using optical flow and clustering

AU - Luong, Huynh Van

AU - Rakêt, Lars Lau

AU - Huang, Xin

AU - Forchhammer, Søren

PY - 2012

Y1 - 2012

N2 - Distributed video coding (DVC) is a coding paradigm that exploits the source statistics at the decoder side to reduce the complexity at the encoder. The coding efficiency of DVC critically depends on the quality of side information generation and accuracy of noise modeling. This paper considers transform domain Wyner-Ziv (TDWZ) coding and proposes using optical flow to improve side information generation and clustering to improve the noise modeling. The optical flow technique is exploited at the decoder side to compensate for weaknesses of block-based methods, when using motion-compensation to generate side information frames. Clustering is introduced to capture cross band correlation and increase local adaptivity in the noise modeling. This paper also proposes techniques to learn from previously decoded WZ frames. Different techniques are combined by calculating a number of candidate soft side information for low density parity check accumulate decoding. The proposed decoder side techniques for side information and noise learning (SING) are integrated in a TDWZ scheme. On test sequences, the proposed SING codec robustly improves the coding efficiency of TDWZ DVC. For WZ frames using a GOP size of 2, up to 4-dB improvement or an average (Bjontegaard) bit-rate savings of 37% is achieved compared with DISCOVER.

AB - Distributed video coding (DVC) is a coding paradigm that exploits the source statistics at the decoder side to reduce the complexity at the encoder. The coding efficiency of DVC critically depends on the quality of side information generation and accuracy of noise modeling. This paper considers transform domain Wyner-Ziv (TDWZ) coding and proposes using optical flow to improve side information generation and clustering to improve the noise modeling. The optical flow technique is exploited at the decoder side to compensate for weaknesses of block-based methods, when using motion-compensation to generate side information frames. Clustering is introduced to capture cross band correlation and increase local adaptivity in the noise modeling. This paper also proposes techniques to learn from previously decoded WZ frames. Different techniques are combined by calculating a number of candidate soft side information for low density parity check accumulate decoding. The proposed decoder side techniques for side information and noise learning (SING) are integrated in a TDWZ scheme. On test sequences, the proposed SING codec robustly improves the coding efficiency of TDWZ DVC. For WZ frames using a GOP size of 2, up to 4-dB improvement or an average (Bjontegaard) bit-rate savings of 37% is achieved compared with DISCOVER.

U2 - 10.1109/TIP.2012.2215621

DO - 10.1109/TIP.2012.2215621

M3 - Journal article

C2 - 22949062

SN - 1057-7149

VL - 21

SP - 4782

EP - 4796

JO - IEEE Transactions on Image Processing

JF - IEEE Transactions on Image Processing

IS - 12

ER -