Point clouds are a general format for representing realistic 3D objects in
diverse 3D applications. Since point clouds have large data sizes, developing
efficient point cloud compression methods is crucial. However, excessive
compression leads to various distortions, which deteriorates the point cloud
quality perceived by end users. Thus, establishing reliable point cloud quality
assessment (PCQA) methods is essential as a benchmark to develop efficient
compression methods. This paper presents an accurate full-reference point cloud
quality assessment (FR-PCQA) method called full-reference quality assessment
using support vector regression (FRSVR) for various types of degradations such
as compression distortion, Gaussian noise, and down-sampling. The proposed
method demonstrates accurate PCQA by integrating five FR-based metrics covering
various types of errors (e.g., considering geometric distortion, color
distortion, and point count) using support vector regression (SVR). Moreover,
the proposed method achieves a superior trade-off between accuracy and
calculation speed because it includes only the calculation of these five simple
metrics and SVR, which can perform fast prediction. Experimental results with
three types of open datasets show that the proposed method is more accurate
than conventional FR-PCQA methods. In addition, the proposed method is faster
than state-of-the-art methods that utilize complicated features such as
curvature and multi-scale features. Thus, the proposed method provides
excellent performance in terms of the accuracy of PCQA and processing speed.
Our method is available from this https URL

该研究提出了一种基于支持向量回归的准确的全参考点云质量评估方法，通过整合五种基于全参考的度量标准，并使用支持向量回归进行快速预测，实现了对压缩失真、高斯噪声和降采样等多种降级类型的准确评估，其性能优于传统的全参考点云质量评估方法，并且比使用复杂特征（如曲率和多尺度特征）的最新方法更快速和精确。

基于支持向量回归的完整参考点云质量评估

Full reference point cloud quality assessment using support vector  regression

Recent years have seen steady growth in the popularity and availability of
High Dynamic Range (HDR) content, particularly videos, streamed over the
internet. As a result, assessing the subjective quality of HDR videos, which
are generally subjected to compression, is of increasing importance. In
particular, we target the task of full-reference quality assessment of
compressed HDR videos. The state-of-the-art (SOTA) approach HDRMAX involves
augmenting off-the-shelf video quality models, such as VMAF, with features
computed on non-linearly transformed video frames. However, HDRMAX increases
the computational complexity of models like VMAF. Here, we show that an
efficient class of video quality prediction models named FUNQUE+ achieves SOTA
accuracy. This shows that the FUNQUE+ models are flexible alternatives to VMAF
that achieve higher HDR video quality prediction accuracy at lower
computational cost.

最近几年，网络上诸如高动态范围（HDR）视频等的流媒体内容越来越受欢迎和可用。因此，评估通常经过压缩的 HDR 视频的主观质量变得越来越重要。本文针对压缩 HDR 视频的全参考质量评估这个任务，介绍了名为 FUNQUE + 的高效视频质量预测模型，其能够以更低的计算成本实现超越当前最先进方法 HDRMAX 的预测准确性。