A computationally-simplified and descriptor-richer Point Cloud Quality
Assessment (PCQA) metric, namely PointPCA+, is proposed in this paper, which is
an extension of PointPCA. PointPCA proposed a set of perceptually-relevant
descriptors based on PCA decomposition that were applied to both the geometry
and texture data of point clouds for full reference PCQA. PointPCA+ employs PCA
only on the geometry data while enriching existing geometry and texture
descriptors, that are computed more efficiently. Similarly to PointPCA, a total
quality score is obtained through a learning-based fusion of individual
predictions from geometry and texture descriptors that capture local shape and
appearance properties, respectively. Before feature fusion, a feature selection
module is introduced to choose the most effective features from a proposed
super-set. Experimental results show that PointPCA+ achieves high predictive
performance against subjective ground truth scores obtained from publicly
available datasets. The code is available at
https://github.com/cwi-dis/pointpca_suite/.

本文提出了一个计算简化和描述符丰富的点云质量评估（PCQA）指标，名为 PointPCA+，它是 PointPCA 的扩展。PointPCA 提出了一组基于 PCA 分解的感知相关描述符，这些描述符应用于点云的几何和纹理数据以进行完全参考的 PCQA。PointPCA + 仅在几何数据上使用 PCA，同时丰富了现有的几何和纹理描述符，计算效率更高。与 PointPCA 类似，通过学习融合几何和纹理描述符的个别预测来获取总体质量分数，捕捉局部形状和外观特性。在特征融合之前，引入了特征选择模块，从提出的超级集中选择最有效的特征。实验结果表明，PointPCA + 对公开可用数据集获得的主观真实分数具有较高的预测性能。代码可在 https://github.com/cwi-dis/pointpca_suite/ 上获得。

PointPCA+：扩展的 PointPCA 客观质量评估度量

PointPCA+: Extending PointPCA objective quality assessment metric

Nowadays, 2D barcodes have been widely used for advertisement, mobile
payment, and product authentication. However, in applications related to
product authentication, an authentic 2D barcode can be illegally copied and
attached to a counterfeited product in such a way to bypass the authentication
scheme. In this paper, we employ a proprietary 2D barcode pattern and use
multimedia forensics methods to analyse the scanning and printing artefacts
resulting from the copy (rebroadcasting) attack. A diverse and complementary
feature set is proposed to quantify the barcode texture distortions introduced
during the illegal copying process. The proposed features are composed of
global and local descriptors, which characterize the multi-scale texture
appearance and the points of interest distribution, respectively. The proposed
descriptors are compared against some existing texture descriptors and deep
learning-based approaches under various scenarios, such as cross-datasets and
cross-size. Experimental results highlight the practicality of the proposed
method in real-world settings.

本研究提出了一种用于鉴别商品的 2D 条码纹理鉴定方法，并对多媒体取证方法进行了分析，将全局和局部描述符与其他纹理描述符和基于深度学习的方法进行了比较，结果表明该方法在实际应用中具有良好的效果。

融合多尺度纹理和剩余描述符的多层次 2D 条形码转播检测

Fusing Multiscale Texture and Residual Descriptors for Multilevel 2D Barcode Rebroadcasting Detection

In this paper, we propose a multi-level texture encoding and representation
network (MuLTER) for texture-related applications. Based on a multi-level
pooling architecture, the MuLTER network simultaneously leverages low- and
high-level features to maintain both texture details and spatial information.
Such a pooling architecture involves few extra parameters and keeps feature
dimensions fixed despite of the changes of image sizes. In comparison with
state-of-the-art texture descriptors, the MuLTER network yields higher
recognition accuracy on typical texture datasets such as MINC-2500 and
GTOS-mobile with a discriminative and compact representation. In addition, we
analyze the impact of combining features from different levels, which supports
our claim that the fusion of multi-level features efficiently enhances
recognition performance. Our source code will be published on GitHub
(this https URL).

本研究提出了一种多层纹理编码和表示网络（MuLTER），通过多层池化架构，该网络同时利用低级和高级特征以保持纹理细节和空间信息，并支持不同层级特征的融合，从而在纹理描述符领域提高了识别精度。