The scalable high efficiency video coding (SHVC) is an extension of high efficiency video coding (HEVC). It introduces multiple layers and inter-layer prediction, thus significantly increases the coding complexity on top of the already complicated HEVC encoder. In inter prediction for quality SHVC, in order to determine the best possible mode at each depth level, a coding tree unit can be recursively split into four depth levels, including merge mode, inter2N×2N, inter2N×N, interN×2N, interN×N, inter2N×nU, inter2N×nD, internL×2N and internRx×2N, intra modes and inter-layer reference (ILR) mode. This can obtain the highest coding efficiency, but also result in very high coding complexity. Therefore, it is crucial to improve coding speed while maintaining coding efficiency. In this research, we have proposed a new depth level and inter mode prediction algorithm for quality SHVC. First, the depth level candidates are predicted based on inter-layer correlation, spatial correlation and its correlation degree. Second, for a given depth candidate, we divide mode prediction into square and non-square mode predictions respectively. Third, in the square mode prediction, ILR and merge modes are predicted according to depth correlation, and early terminated whether residual distribution follows a Gaussian distribution. Moreover, ILR mode, merge mode and inter2N×2N are early terminated based on significant differences in Rate Distortion (RD) costs. Fourth, if the early termination condition cannot be satisfied, non-square modes are further predicted based on significant differences in expected values of residual coefficients. Finally, inter-layer and spatial correlations are combined with residual distribution to examine whether to early terminate depth selection. Experimental results have demonstrated that, on average, the proposed algorithm can achieve a time saving of 71.14%, with a bit rate increase of 1.27%.

中文翻译：

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用于质量可扩展的高效视频编码的快速深度和帧间模式预测

可伸缩高效视频编码（SHVC）是高效视频编码（HEVC）的扩展。它引入了多层和层间预测，从而在已经很复杂的 HEVC 编码器之上显着增加了编码复杂度。在质量SHVC的帧间预测中，为了确定每个深度级别的最佳可能模式，可以将编码树单元递归拆分为四个深度级别，包括合并模式、inter2N×2N、inter2N×N、interN×2N、interN ×N、inter2N×nU、inter2N×nD、internL×2N和internRx×2N、帧内模式和层间参考（ILR）模式。这样可以获得最高的编码效率，但也导致了非常高的编码复杂度。因此，在保持编码效率的同时提高编码速度至关重要。在这项研究中，我们为高质量的 SHVC 提出了一种新的深度级别和模式间预测算法。首先，基于层间相关性、空间相关性及其相关度来预测深度​​级别候选者。其次，对于给定的深度候选，我们分别将模式预测分为方形和非方形模式预测。第三，在平方模式预测中，根据深度相关性预测ILR和合并模式，并提前终止残差分布是否遵循高斯分布。此外，基于速率失真 (RD) 成本的显着差异，ILR 模式、合并模式和 inter2N×2N 被提前终止。第四，如果不能满足提前终止条件，则根据残差系数期望值的显着差异进一步预测非方模。最后，层间和空间相关性与残差分布相结合，以检查是否提前终止深度选择。实验结果表明，该算法平均可节省71.14%的时间，比特率提高1.27%。