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.

中文翻译：

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用于高效视频编码的最大边距马尔可夫网络中具有判别学习的结构化集帧内预测

本文提出了一种用于高效视频编码 (HEVC) 的帧内编码的新模型，该模型同时预测具有最佳率失真的像素块。它利用空间统计相关性进行基于二维上下文的最优预测，此外还制定了数据驱动的结构相互依赖性，使预测误差与概率分布相一致，这对于成功的变换和编码是可取的。结构化集预测模型结合了最大边距马尔可夫网络 (M3N) 来调节和优化多个块预测。模型参数是通过从其他可能的估计中区分实际像素值来学习的，以最大化裕度（即决策边界带宽）。与专注于最小化预测误差的现有方法相比，基于 M3N 的模型自适应地保持一组预测的一致性。具体来说，所提出的模型通过将单个块的损失与后续离散余弦变换系数的联合分布相关联来同时优化一组预测。当样本量增加时，在可分解损失函数下，预测误差渐近上限为训练误差。作为内部步骤，我们优化了底层马尔可夫网络结构，以找到使用期望传播实现最大能量的状态。为了验证，我们将所提出的模型集成到 HEVC 中，以进行率失真优化的最佳模式选择。与 HEVC 帧内编码相比，所提出的预测模型获得了高达 2.85% 的比特率降低并实现了更好的视觉质量。