Deep learning-based quality assessments have significantly enhanced
perceptual multimedia quality assessment, however it is still in the early
stages for 3D visual data such as 3D point clouds (PCs). Due to the high volume
of 3D-PCs, such quantities are frequently compressed for transmission and
viewing, which may affect perceived quality. Therefore, we propose no-reference
quality metric of a given 3D-PC. Comparing to existing methods that mostly
focus on geometry or color aspects, we propose integrating frequency magnitudes
as indicator of spatial degradation patterns caused by the compression. To map
the input attributes to quality score, we use a light-weight hybrid deep model;
combined of Deformable Convolutional Network (DCN) and Vision Transformers
(ViT). Experiments are carried out on ICIP20 [1], PointXR [2] dataset, and a
new big dataset called BASICS [3]. The results show that our approach
outperforms state-of-the-art NR-PCQA measures and even some FR-PCQA on PointXR.
The implementation code can be found at: this https URL

基于深度学习的质量评估在感知多媒体质量评估方面取得了显著的提升，然而对于 3D 点云等 3D 视觉数据仍处于初级阶段。因此，我们提出了一种给定 3D 点云的无参考质量度量方法，通过集成频率幅度作为空间降解模式的指标来评估由压缩引起的质量影响，并使用轻量级混合深度模型进行质量评分映射。实验证明我们的方法在 PointXR 上优于最先进的非参考点云质量评估方法甚至一些全参考点云质量评估方法。

激活频率和 ViT 用于 3D 点云质量评估无需参考

Activating Frequency and ViT for 3D Point Cloud Quality Assessment  without Reference

We present a novel deep learning architecture for fusing static
multi-exposure images. Current multi-exposure fusion (MEF) approaches use
hand-crafted features to fuse input sequence. However, the weak hand-crafted
representations are not robust to varying input conditions. Moreover, they
perform poorly for extreme exposure image pairs. Thus, it is highly desirable
to have a method that is robust to varying input conditions and capable of
handling extreme exposure without artifacts. Deep representations have known to
be robust to input conditions and have shown phenomenal performance in a
supervised setting. However, the stumbling block in using deep learning for MEF
was the lack of sufficient training data and an oracle to provide the
ground-truth for supervision. To address the above issues, we have gathered a
large dataset of multi-exposure image stacks for training and to circumvent the
need for ground truth images, we propose an unsupervised deep learning
framework for MEF utilizing a no-reference quality metric as loss function. The
proposed approach uses a novel CNN architecture trained to learn the fusion
operation without reference ground truth image. The model fuses a set of common
low level features extracted from each image to generate artifact-free
perceptually pleasing results. We perform extensive quantitative and
qualitative evaluation and show that the proposed technique outperforms
existing state-of-the-art approaches for a variety of natural images.

提出了一种新的深度学习架构，用于融合静态多曝光图像，并使用无监督的深度学习框架，利用无参考质量度量作为损失函数进行训练。使用从每个图像中提取的低级特征生成无伪影感知愉悦的结果，这种方法在自然图像方面优于现有的最先进方法。