Optimal intra coding of HEVC by structured set prediction mode with discriminative learning
This paper proposes a novel model on intra-coding for high efficiency video coding (HEVC), which can simultaneously make the set of prediction for block of pixels in an optimal rate-distortion sense. It not only utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the probability distribution which is favorable for subsequent transform and coding. The so-called structured set prediction model incorporates max-margin Markov network to regulate and reason the multiple prediction in the blocks. The model parameters are learned by discriminating the actual pixel value from the other possible estimates to the maximal margin. Distinguished from the existing methods concerning the minimal prediction error, the Markov network is adaptively derived to maintain the coherence of set of prediction. To be concrete, the proposed model seeks the concurrent optimization of the set of prediction by relating the loss function to the probability distribution of subsequent DCT coefficients. The prediction error is demonstrated to be asymptotically upper bounded by the training error under the decomposable loss function. For validation, we integrate the proposed model into HEVC intra coding and experimental results show obvious improvement of coding performance in terms of BD-rate.