Quality Evaluation of Machine Learning-based Point Cloud Coding Solutions

In this paper, a quality evaluation of three point cloud coding solutions based on machine learning technology is presented, notably, ADLPCC, PCC_GEO_CNN, and PCGC, as well as LUT_SR, which uses multi-resolution Look-Up Tables. Moreover, the MPEG G-PCC was used as an anchor. A set of six point clouds, representing both landscapes and objects were coded using the five encoders at different bit rates, and a subjective test, where the distorted and reference point clouds were rotated in a video sequence side by side, is carried out to assess their performance. Furthermore, the performance of point cloud objective quality metrics that usually provide a good representation of the coded content is analyzed against the subjective evaluation results. The obtained results suggest that some of these metrics fail to provide a good representation of the perceived quality, and thus are not suitable to evaluate some distortions created by machine learning-based solutions. A comparison between the analyzed metrics and the type of represented scene or codec is also presented.This research was funded by the Portuguese FCT-Fundação para a Ciência e Tecnologia under the project UIDB/50008/2020, PLive X-0017-LX-20, and by operation Centro-01-0145-FEDER-000019 - C4 - Centro de Competencias em Cloud Computing.info:eu-repo/semantics/acceptedVersio

On the stability of point cloud machine learning based coding

This paper analyses the performance of two of the most well known deep learning-based point cloud coding solutions, considering the training conditions. Several works have recently been published on point cloud machine learning-based coding, following the recent tendency on image coding. These codecs are typically seen as a set of predefined trained machines. However, the performance of such models is usually very dependent of their training, and little work has been considered on the stability of the codecs’ performance, as well as the possible influence of the loss function parameters, and the increasing number of training epochs. The evaluation experiments are supported in a generic test set with point clouds representing objects and also more complex scenes, using the point to point metric (PSNR D1), as several studies revealed the good quality representation of this geometry-only point cloud metric.Research funded by the Portuguese FCT-Fundação para a Ciência e Tecnologia under the project UIDB/50008/2020, PLive X-0017-LX-20, and by operation Centro-01-0145-FEDER-000019 - C4 - Centro de Competencias em Cloud Computing.info:eu-repo/semantics/acceptedVersio

Subjective Quality Evaluation of Point Clouds Using a Head Mounted Display

This paper reports on a subjective quality evaluation of static point clouds encoded with the MPEG codecs V-PCC and G-PCC, the deep learning-based codec RS-DLPCC, and the popular Draco codec. 18 subjects visualized 3D representations of distorted point clouds using a Head Mounted Display, which allowed for a direct comparison with their reference. The Mean Opinion Scores (MOS) obtained in this subjective evaluation were compared with the MOS from two previous studies, where the same content was visualized either on a 2D display or a 3D stereoscopic display, through the Pearson Correlation, Spearman Rank Order Correlation, Root Mean Square Error, and the Outlier Ratio. The results indicate that the three studies are highly correlated with one another. Moreover, a statistical analysis between all evaluations showed no significant differences between them

Motion estimation with chessboard pattern prediction strategy

Due to high correlations among the adjacent blocks, several algorithms utilize movement information of spatially and temporally correlated neighboring blocks to adapt their search patterns to that information. In this paper, this information is used to define a dynamic search pattern. Each frame is divided into two sets, black and white blocks, like a chessboard pattern and a different search pattern, is defined for each set. The advantage of this definition is that the number of spatially neighboring blocks is increased for each current block and it leads to a better prediction for each block. Simulation results show that the proposed algorithm is closer to the Full-Search algorithm in terms of quality metrics such as PSNR than the other state-of-the-art algorithms while at the same time the average number of search points is less.info:eu-repo/semantics/publishedVersio

A Two-Step Segmentation Method for Breast Ultrasound Masses Based on Multi-resolution Analysis

Breast ultrasound images have several attractive properties that make them an interesting tool in breast cancer detection. However, their intrinsic high noise rate and low contrast turn mass detection and segmentation into a challenging task. In this article, a fully automated two-stage breast mass segmentation approach is proposed. In the initial stage, ultrasound images are segmented using support vector machine or discriminant analysis pixel classification with a multiresolution pixel descriptor. The features are extracted using non-linear diffusion, bandpass filtering and scale-variant mean curvature measures. A set of heuristic rules complement the initial segmentation stage, selecting the region of interest in a fully automated manner. In the second segmentation stage, refined segmentation of the area retrieved in the first stage is attempted, using two different techniques. The AdaBoost algorithm uses a descriptor based on scale-variant curvature measures and non-linear diffusion of the original image at lower scales, to improve the spatial accuracy of the ROI. Active contours use the segmentation results from the first stage as initial contours. Results for both proposed segmentation paths were promising, with normalized Dice similarity coefficients of 0.824 for AdaBoost and 0.813 for active contours. Recall rates were 79.6% for AdaBoost and 77.8% for active contours, whereas the precision rate was 89.3% for both methods.info:eu-repo/semantics/publishedVersio

MPEG DASH - some QoE-based insights into the tradeoff between audio and video for live music concert streaming under congested network conditions

The rapid adoption of MPEG-DASH is testament to its core design principles that enable the client to make the informed decision relating to media encoding representations, based on network conditions, device type and preferences. Typically, the focus has mostly been on the different video quality representations rather than audio. However, for device types with small screens, the relative bandwidth budget difference allocated to the two streams may not be that large. This is especially the case if high quality audio is used, and in this scenario, we argue that increased focus should be given to the bit rate representations for audio. Arising from this, we have designed and implemented a subjective experiment to evaluate and analyses the possible effect of using different audio quality levels. In particular, we investigate the possibility of providing reduced audio quality so as to free up bandwidth for video under certain conditions. Thus, the experiment was implemented for live music concert scenarios transmitted over mobile networks, and we suggest that the results will be of significant interest to DASH content creators when considering bandwidth tradeoff between audio and video.info:eu-repo/semantics/publishedVersio

Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features

(C) 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Magnetic Resonance Imaging (MRI) is a non-invasive tool for the clinical assessment of low-prevalence neuromuscular disorders. Automated diagnosis methods might reduce the need for biopsies and provide valuable information on disease follow-up. In this paper, three methods are proposed to classify target muscles in Collagen VI-related myopathy cases, based on their degree of involvement, notably a Convolutional Neural Network, a Fully Connected Network to classify texture features, and a hybrid method combining the two feature sets. The proposed methods were evaluated on axial T1-weighted Turbo Spin-Echo MRI from 26 subjects, including Ullrich Congenital Muscular Dystrophy and Bethlem Myopathy patients at different evolution stages. The hybrid model achieved the best cross-validation results, with a global accuracy of 93.8%, and F-scores of 0.99, 0.82, and 0.95, for healthy, mild and moderate/severe cases, respectively.info:eu-repo/semantics/acceptedVersio

Quality comparison of the HEVC and VP9 encoders performance

This paper reports a comparison between two recent video codecs, namely the HEVC and the VP9, using High Definition Video Sequences encoded with different bit rates. A subjective test for the evaluation of the provided Quality of Experience is reported. The video sequences were shown to a panel of subjects on a High Definition LED display and the subjective tests were performed using a Single Stimulus Methodology. The results shown that the HEVC encoder provides a better visual quality on low bit rates than the VP9. Similar performance was obtained for visually lossless conditions, although the HEVC requires lower bit rates to reach that level. Moreover, the correlation of the subjective evaluation and three tested objective metrics (PSNR, SSIM, and FSIM) revealed a good representation of the subjective results, particularly the SSIM and the FSIM metrics.info:eu-repo/semantics/publishedVersio

Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Magnetic Resonance Imaging (MRI) is a non-invasive tool for the clinical assessment of low-prevalence neuromuscular disorders. Automated diagnosis methods might reduce the need for biopsies and provide valuable information on disease follow-up. In this paper, three methods are proposed to classify target muscles in Collagen VI-related myopathy cases, based on their degree of involvement, notably a Convolutional Neural Network, a Fully Connected Network to classify texture features, and a hybrid method combining the two feature sets. The proposed methods were evaluated on axial T1-weighted Turbo Spin-Echo MRI from 26 subjects, including Ullrich Congenital Muscular Dystrophy and Bethlem Myopathy patients at different evolution stages. The hybrid model achieved the best cross-validation results, with a global accuracy of 93.8%, and F-scores of 0.99, 0.82, and 0.95, for healthy, mild and moderate/severe cases, respectively.info:eu-repo/semantics/acceptedVersio

Assessment of speckle denoising filters for digital holography using subjective and objective evaluation models

Digital holography is an emerging imaging technique for displaying and sensing three dimensional objects. The perceived image quality of a hologram is frequently corrupted by speckle noise due to coherent illumination. Although several speckle noise reduction methods have been developed so far, there are scarce quality assessment studies to address their performance and they typically focus solely on objective metrics. However, these metrics do not reflect the visual quality perceived by a human observer. In this work, the performance of four speckle reduction algorithms, namely the nonlocal means, the Lee, the Frost and the block matching 3D filters, with varying parameterizations, were subjectively evaluated. The results were ranked with respect to the perceived image quality to obtain the mean opinion scores using pairwise comparison. The correlation between the subjective results and twenty different no-reference objective quality metrics was evaluated. The experiment indicates that block matching 3D and Lee are the preferred filters, depending on hologram characteristics. The best performing objective metrics were identified for each filter.info:eu-repo/semantics/publishedVersio