目的 高效视频编码(HEVC)采用率失真优化技术选择最佳的编码参数,实现编码比特率和视频图像失真之间的平衡。失真度量通常采用均方误差和绝对误差和,这些方法并没有考虑人眼的主观感受。为了提高视频编码的主观感知质量,提出一个融合视觉感知特性的率失真优化算法,并应用于帧间率失真优化过程中。方法 首先定义了一个视觉感知因子,该因子考虑了人类视觉系统对视频图像的空域活动性区域、纹理区域、时域运动性区域和亮度的感知特性,然后以编码树单元为单位对拉格朗日乘子进行自适应调整,最后根据拉格朗日乘子与量化参数之间的关系,对量化参数做进一步的修正。结果 与HEVC参考软件相比,本文算法明显提高了率失真性能,对于相同的结构相似度(SSIM)分值,本文算法在随机访问和低延时配置下平均分别节省3.1%,4.9%的码率,最高能节省9.0%的码率。与代表性文献算法相比,对于相同的SSIM,本文算法在随机访问和低延时配置下平均分别增加了0.7%,2.2%的码率节省。结论 本文率失真优化策略能够根据图像不同的视觉特性自适应的调整率失真优化过程中的拉格朗日乘子,在保持编码质量基本不变的情况下,节省了码率,提高了HEVC的编码性能。

Objective Rate distortion optimization (RDO) techniques play an important role in high-efficiency video coding (HEVC) because these techniques are usually used to select optimal encoding parameters and implement the trade-off between bit-rate and distortion of reconstructed video. In HEVC, distortion is usually evaluated by mean squared error or sum of absolute difference because they are well-understood quantities and convenient. However, they do not clearly reflect human visual perception because video signals are ultimately received by human eyes. Thus, Lagrangian multiplier must be developed by considering human visual system characteristics. Method In this paper, a novel RDO algorithm is proposed based on human perception and is applied for inter prediction video coding. First, a human perception factor was defined by the features of human vision system about spatial motion region, texture region, temporal motion activity, and illumination in video. Multiplication of spatial motion region, texture region, temporal motion activity, and illumination factors were used to evaluate human perception. Then, Lagrangian multiplier was adaptively adjusted for each coding tree unit based on human perception factor. Finally, through derivation, we determined that variation of Lagrangian multiplier results in the change of quantization parameters. Hence, quantization parameters were further amended according to the relationship between Lagrangian multiplier and quantization parameters. Result Experimental results showed that the proposed method improves rate-distortion performance compared with HEVC reference software. For the same structural similarity (SSIM) index, the proposed approach saves bit by 3.1% for random access configuration and 4.9% on average for low delay encoding configuration. Maximal bit rate saving can reach 9.0%. The rate-distortion performance of the proposed approach is better than that of representative literature algorithm. For the same SSIM index, the proposed approach can save the bit by 0.7% for random access configuration and 2.2% for low delay encoding configuration on average. We conducted an experiment to explain the role of each perceptual factor. Experimental results showed that the factor of texture region is better than other factors and is not so good as the combination of four factors. Conclusion In this paper, we proposed a novel RDO strategy that can adaptively adjust Lagrange multipliers based on different visual characteristics. Experimental results showed that the proposed strategy can improve HEVC coding performance and can achieve bit rate saving with the same reconstructed video quality.