It has been recognized that videos have to be encoded in a rate-distortion optimized manner for high coding performance. Therefore, operational coding methods have been developed for conventional distortion metrics such as Sum of Squared Error (SSE). Nowadays, with the rapid development of machine learning, the state-of-the-art learning based metric Video Multimethod Assessment Fusion (VMAF) has been proven to outperform conventional ones in terms of the correlation with human perception, and thus deserves integration into the coding framework. However, unlike conventional metrics, VMAF has no specific computational formulas and may be frequently updated by new training data, which invalidates the existing coding methods and makes it highly desired to develop a rate-distortion optimized method for VMAF. Moreover, VMAF is designed to operate at the frame level, which leads to further difficulties in its application to today’s block based coding. In this paper, we propose a VMAF oriented perceptual coding method based on piecewise metric coupling. Firstly, we explore the correlation between VMAF and SSE in the neighborhood of a benchmark distortion. Then a rate-distortion optimization model is formulated based on the correlation, and an optimized block based coding method is presented for VMAF. Experimental results show that 3.61% and 2.67% bit saving on average can be achieved for VMAF under the low_delay_p and the random_access_main configurations of HEVC coding respectively.

中文翻译：

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基于分段度量耦合的面向VMAF的感知编码


人们已经认识到，为了获得高编码性能，视频必须以率失真优化的方式进行编码。因此，针对传统失真度量（例如误差平方和（SSE））开发了运算编码方法。如今，随着机器学习的快速发展，最先进的基于学习的度量视频多方法评估融合（VMAF）已被证明在与人类感知的相关性方面优于传统方法，因此值得集成到机器学习中。编码框架。然而，与传统的度量不同，VMAF没有特定的计算公式，并且可能会被新的训练数据频繁更新，这使得现有的编码方法失效，因此非常需要开发一种针对VMAF的率失真优化方法。此外，VMAF被设计为在帧级操作，这导致其在当今基于块的编码中的应用更加困难。在本文中，我们提出了一种基于分段度量耦合的面向VMAF的感知编码方法。首先，我们探讨了 VMAF 和 SSE 在基准失真附近的相关性。然后基于相关性建立了率失真优化模型，并提出了一种基于VMAF优化的块编码方法。实验结果表明，在 HEVC 编码的 low_delay_p 和 random_access_main 配置下，VMAF 平均可以分别实现 3.61% 和 2.67% 的比特节省。