论文标题
参考感知质量模型减少了3D点云压缩的速率控制的应用
Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compression
论文作者
论文摘要
在速率优化中，编码器设置是通过最大化重建质量度量来确定的，但受到比特率的约束。这种方法的主要挑战之一是定义可以以低计算成本计算的质量度量，并且与知觉质量息息相关。尽管已经为图像和视频开发了一些符合这两个标准的质量措施，但对于3D点云而没有这样的质量措施。我们通过提出一个线性感知质量模型来解决基于视频的点云压缩（V-PCC）标准的限制，该模型的变量是V-PCC几何和颜色量化参数，并且可以从从原始3D点云中提取的两个功能轻松计算其系数。具有400个压缩3D点云的主观质量测试表明，所提出的模型与平均意见分数良好相关，在Spearman Rank-rorder和Pearsons线性相关系数方面优于最先进的完整参考目标度量。此外，我们表明，基于提议的模型，基于提议的模型的评级优化为基于详尽的搜索提供了更高的感知质量，并具有点对点客观质量度量标准。
In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bit rate. One of the main challenges of this approach is to define a quality measure that can be computed with low computational cost and which correlates well with perceptual quality. While several quality measures that fulfil these two criteria have been developed for images and video, no such one exists for 3D point clouds. We address this limitation for the video-based point cloud compression (V-PCC) standard by proposing a linear perceptual quality model whose variables are the V-PCC geometry and color quantization parameters and whose coefficients can easily be computed from two features extracted from the original 3D point cloud. Subjective quality tests with 400 compressed 3D point clouds show that the proposed model correlates well with the mean opinion score, outperforming state-of-the-art full reference objective measures in terms of Spearman rank-order and Pearsons linear correlation coefficient. Moreover, we show that for the same target bit rate, ratedistortion optimization based on the proposed model offers higher perceptual quality than rate-distortion optimization based on exhaustive search with a point-to-point objective quality metric.
