Side Information and Noise Learning for Distributed Video Coding using Optical Flow and Clustering

Publication: Research - peer-reviewJournal article – Annual report year: 2012

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Distributed video coding (DVC) is a coding paradigm which 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 noise modeling. The optical flow technique is exploited at the decoder side to compensate 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 (LDPCA) 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 4dB improvement or an average (Bjøntegaard) bit-rate saving of 37% is achieved compared with DISCOVER.
Original languageEnglish
JournalI E E E Transactions on Image Processing
Publication date2012
Volume21
Journal number12
Pages4782 - 4796
Number of pages14
ISSN1057-7149
DOIs
StatePublished
CitationsWeb of Science® Times Cited: 2

Keywords

  • Distributed video coding, Noise residual learning, Optical flow, Adaptive noise, Multi-hypothesis
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