论文标题
可解释基于机器学习的转换编码，用于高效插入预测
Explainable Machine Learning based Transform Coding for High Efficiency Intra Prediction
论文作者
论文摘要
机器学习技术为探索转换的编码性能潜力提供了机会。在这项工作中，我们提出了一种基于可解释的基于转换的视频编码，以提高编码效率。首先，我们将基于机器学习的转换设计建模为最大化能量压实或去相关能力的优化问题。分析了基于机器学习的转换，即具有调整后偏置（SAAB）变换的子空间近似值，并与主流离散余弦变换（DCT）进行了比较，并在其能量压实和去相关功能上进行了比较。其次，我们建议使用离线SAAB变换学习的基于SAAB变换的视频编码框架。同时，开发了依赖于内部模式的SAAB变换。然后，基于SAAB变换的视频内部编码的速率失真（RD）在理论上和实验上进行了详细分析。最后，开发了在视频编码中集成SAAB变换和DCT的三种策略，以提高编码效率。实验结果表明，提出的8 $ \ times $ 8基于SAAB转换的视频编码可以实现BjønteggardDelta比特率（BDBR），从-1.19％到-10.00％至-10.00％至-10.00％和-3.07％，与主流8 $ 8 $ \ times 8 dct的编码方案相比。
Machine learning techniques provide a chance to explore the coding performance potential of transform. In this work, we propose an explainable transform based intra video coding to improve the coding efficiency. Firstly, we model machine learning based transform design as an optimization problem of maximizing the energy compaction or decorrelation capability. The explainable machine learning based transform, i.e., Subspace Approximation with Adjusted Bias (Saab) transform, is analyzed and compared with the mainstream Discrete Cosine Transform (DCT) on their energy compaction and decorrelation capabilities. Secondly, we propose a Saab transform based intra video coding framework with off-line Saab transform learning. Meanwhile, intra mode dependent Saab transform is developed. Then, Rate Distortion (RD) gain of Saab transform based intra video coding is theoretically and experimentally analyzed in detail. Finally, three strategies on integrating the Saab transform and DCT in intra video coding are developed to improve the coding efficiency. Experimental results demonstrate that the proposed 8$\times$8 Saab transform based intra video coding can achieve Bjønteggard Delta Bit Rate (BDBR) from -1.19% to -10.00% and -3.07% on average as compared with the mainstream 8$\times$8 DCT based coding scheme.
