A fast and accurate assessment of visual masking effects is desirable while encoding in order to utilize such effects to improve the quality of compressed videos through an adaptive quantization (AQ) scheme. Here, we propose a method of estimating the contrast masking threshold on natural scene patches, using texture cues imparted by steerable filter responses. We then employ the estimated thresholds to perform AQ for AV1 encoding. Our experimental results establish that the proposed method is able to outperform existing visual masking models in terms of estimation performance while being relatively computationally inexpensive than these models, and is also able to improve the variance based AQ algorithm that is currently deployed in the SVT-AV1 codec. Using the multi-scale structural similarity index measure (MS-SSIM) as the quality model, our approach achieves an average BD-rate of -1.82% using the uniform quantization scheme as anchor as compared to 5.83% obtained with the variance based method. We note that the proposed approach produces less visible compression artifacts than the variance based AQ approach at lower bitrates, while maintaining similar encoding complexity.

在编码时需要对视觉掩蔽效果进行快速而准确的评估，以便利用这种效果通过自适应量化（AQ）方案来改善压缩视频的质量。在这里，我们提出了一种使用可控滤镜响应所赋予的纹理提示来估计自然场景斑块上的对比度掩蔽阈值的方法。然后，我们使用估计的阈值来执行AV1编码的AQ。我们的实验结果表明，所提出的方法在估计性能方面可以胜过现有的视觉蒙版模型，同时与这些模型相比在计算上相对便宜，并且还可以改善当前在SVT-AV1中部署的基于方差的AQ算法编解码器。使用多尺度结构相似性指标度量（MS-SSIM）作为质量模型，与采用基于方差的方法获得的5.83％相比，使用统一量化方案作为锚点，我们的方法可实现-1.82％的平均BD率。我们注意到，在保持相似的编码复杂度的同时，与基于方差的AQ方法相比，所提出的方法在较低的比特率下产生的可见压缩伪像更少。