XLR (piXel Loss Rate): A Lightweight Indicator to Measure Video QoE in IP Networks

Díaz, César; Pérez, Pablo; Cabrera, Julián; Ruiz, Jaime; García, Narciso

doi:10.1109/tnsm.2020.2980752

Cited by 5 publications

(4 citation statements)

References 45 publications

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“…The purpose was to show that the subjective quality of a video cannot be predicted from the visual quality of the frame alone when some hidden error occurs. In [26], a new objective indicator, the pixel loss rate (XLR), was proposed. It evaluates the packet loss rate during video streaming.…”

Section: Related Workmentioning

confidence: 99%

UHD Database Focus on Smart Cities and Smart Transport

Sevcik,

Uhrina,

Frnda

2024

Electronics

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“Smart city” refers to a modern solution to organizing a city’s services, using cloud technologies to collect and evaluate large amounts of data, including data from camera systems. Smart city management covers several areas that can be implemented separately, but only their combination can realize the overall desired smart city function. One of the core areas of smart city automation is smart city transport. Transportation is a crucial system in any city, and this is why it needs to be monitored. The primary objective of this publication is to generate top-notch 4K UHD video sequences that are solely dedicated to showcasing smart cities and their transportation systems. The resulting comprehensive database will be made accessible to all professionals in the field, who can utilize it for extensive research purposes. Additionally, all the reference video sequences will be transcoded into various quality settings by altering critical parameters like the resolution, compression standard, and bit rate. The ultimate aim is to determine the best combination of video parameters and their respective settings based on the measured values. This in-depth evaluation will ensure that each video sequence is of the highest quality and provides an unparalleled experience for the service providers offering the service. The video sequences captured will be analyzed for quality assessments in smart cities or smart transport technologies. The database will also include objective and subjective ratings, along with information about the dynamics determined by spatial and temporal information. This will enable a comparison of the subjective evaluation of a selected sample of our respondents with the work of other researchers, who may evaluate it with a different sample of evaluators. The assumption of our future research is to predict the subjective quality based on the type of sequence determined by its dynamicity.

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Section: Related Workmentioning

confidence: 99%

UHD Database Focus on Smart Cities and Smart Transport

Sevcik,

Uhrina,

Frnda

2024

Electronics

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“…Ref. [ 12 ] proposed XLR (Pixel Loss Rate), a new key quality indicator for video distribution applications. The suggested indicator offers comparable results with existing full reference measures, incorporates the effects of transmission errors in the received video and has a high correlation with subjective MOS scores.…”

Section: Related Workmentioning

confidence: 99%

Subjective Quality Assessment of V-PCC-Compressed Dynamic Point Clouds Degraded by Packet Losses

Dumic

Cruz

2023

Sensors

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This article describes an empirical exploration on the effect of information loss affecting compressed representations of dynamic point clouds on the subjective quality of the reconstructed point clouds. The study involved compressing a set of test dynamic point clouds using the MPEG V-PCC (Video-based Point Cloud Compression) codec at 5 different levels of compression and applying simulated packet losses with three packet loss rates (0.5%, 1% and 2%) to the V-PCC sub-bitstreams prior to decoding and reconstructing the dynamic point clouds. The recovered dynamic point clouds qualities were then assessed by human observers in experiments conducted at two research laboratories in Croatia and Portugal, to collect MOS (Mean Opinion Score) values. These scores were subject to a set of statistical analyses to measure the degree of correlation of the data from the two laboratories, as well as the degree of correlation between the MOS values and a selection of objective quality measures, while taking into account compression level and packet loss rates. The subjective quality measures considered, all of the full-reference type, included point cloud specific measures, as well as others adapted from image and video quality measures. In the case of image-based quality measures, FSIM (Feature Similarity index), MSE (Mean Squared Error), and SSIM (Structural Similarity index) yielded the highest correlation with subjective scores in both laboratories, while PCQM (Point Cloud Quality Metric) showed the highest correlation among all point cloud-specific objective measures. The study showed that even 0.5% packet loss rates reduce the decoded point clouds subjective quality by more than 1 to 1.5 MOS scale units, pointing out the need to adequately protect the bitstreams against losses. The results also showed that the degradations in V-PCC occupancy and geometry sub-bitstreams have significantly higher (negative) impact on decoded point cloud subjective quality than degradations of the attribute sub-bitstream.

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“…It is well known that packet loss effect is directly related with packet loss rate [36]. In particular, in H.264 and later codecs, this effect is related to packet loss burst rate and the coding structure (intra frame refresh period) [32], which are the main components of the "Loss Magnitude" or "piXel Loss Rate" [12]:…”

Section: Video Coding Qualitymentioning

confidence: 99%

“…It leads to coding latencies of T cod = N cod /F . Parameters m 0 and m 1 for packet loss effect have been computed from [12]. Finally, the values for T m that we have used are T m = "Max.…”

Section: Parametrization Of the Different Use Casesmentioning

confidence: 99%

A parametric quality model to evaluate the performance of tele-operated driving services over 5G networks

Pérez

Ruiz

Benito

et al. 2021

Multimed Tools Appl

Self Cite

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We have developed a parametric model to quantify the Key Quality Indicators which affect video-based Tele-operated Driving (ToD) over a mobile network, as well as their relationship with the network Key Performance Indicators. This model can be easily used to specify Quality of Service policies (e.g. through network slicing) that guarantee the required conditions for remote driving on specific areas. We have used our model to validate the feasibility of deploying remote-assisted driving in different real networks, both from current 4G deployments and from pre-commercial and commercial 5G pilots. Our results show that some ToD services (supervision and, up to some point, parking) may be feasible with high-end existing 5G networks. However, full remote driving requires some improvements in the system, particularly to reduce end-to-end latency, increase uplink performance, and minimize service losses. Both the model and its results will be used in the framework of European Union H2020 project 5G-MOBIX to deploy a ToD proof-of-concept in the cross-border corridor between Spain and Portugal.

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XLR (piXel Loss Rate): A Lightweight Indicator to Measure Video QoE in IP Networks

Cited by 5 publications

References 45 publications

UHD Database Focus on Smart Cities and Smart Transport

UHD Database Focus on Smart Cities and Smart Transport

Subjective Quality Assessment of V-PCC-Compressed Dynamic Point Clouds Degraded by Packet Losses

A parametric quality model to evaluate the performance of tele-operated driving services over 5G networks

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