Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding.

TitleStructured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding.
Publication TypeJournal Article
Year of Publication2013
AuthorsDai, W, Xiong, H, Jiang, X, Chen, CWen
JournalIEEE Trans Circuits Syst Video Technol
Volume23
Issue11
Pagination1941-1956
Date Published2013 Nov
ISSN1051-8215
iDASH CategoryPrivacy Technology
Abstract<p>This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding.</p>
DOI10.1109/TCSVT.2013.2269776
Alternate JournalIEEE Trans Circuits Syst Video Technol
PubMed ID25505829
PubMed Central IDPMC4260422
Grant ListK99 LM011392 / LM / NLM NIH HHS / United States
U54 HL108460 / HL / NHLBI NIH HHS / United States