Impact of Packet Loss Rate on Quality of Compressed High Resolution Videos

Bienik, Juraj; Uhrina, Miroslav; Sevcik, Lukas; Holesova, A.

doi:10.3390/s23052744

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…Article [ 10 ] reports how packet losses affect the quality of video sequences compressed with different settings and resolutions. A dataset was created of 11,200 full HD and Ultra HD video sequences encoded with the H.264/AVC and H.265/HEVC encoders at five bit rates and a simulated packet loss rate (PLR) ranging from 0 to 1%.…”

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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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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“…One prominent challenge relates to the subjective nature of determining whether a user's experience falls into the "good" or "bad" category. For instance, consistent packet loss [9] or high latency during video streaming or web browsing might be deemed as negative, while high throughput and low latency during online gaming [10] could be seen as positive. This inherent ambiguity in labeling UGE and UBE data points, lacking clear evaluation criteria, adds complexity to the problem.…”

Section: Introductionmentioning

confidence: 99%

Enhancing user experience in home networks with machine learning-based classification

Rushat Rai,

Thomas Basikolo

2024

ITU J-FET

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With the rapid development of mobile Internet, home broadband has been integrated into people's daily lives, and the market has become increasingly saturated. User experience and broadband quality have become the key factors determining market competitiveness, and consequently, most operators currently are increasing attention to network quality issues and how to improve user experience. This paper proposes an efficient machine learning model to accurately evaluate home user network experiences. The dataset used encompasses network indicator data from 500 anonymized users, and presents a set of formidable challenges including a non-standard sampling rate and time range, an uneven distribution of observations, multiple recorded observations for identical timestamps, a constrained sample size, a subjective definition of Internet experience, and a lack of essential information regarding the data collection setup. Our novel time series characteristic-based method extracts thousands of descriptive statistics from the time series sequences which reveal that, even in the face of the dataset's inherent complexities, our proposed method excels, achieving an impressive 67% validation accuracy. This represents a substantial 3% enhancement over the performance of conventional models on this dataset. Furthermore, we explore the potential of a Recurrent Neural Network (RNN) model, which also yields promising results with a validation accuracy of 58%. It is important to underscore that the performance of the RNN model could be substantially enhanced with a larger dataset. [...]

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“…The authors of [ 7 ] investigate the detrimental effects of packet loss on the video quality encoded using different combinations of compression parameters and resolutions. Their research utilizes a dataset comprising 11,200 full HD and ultra HD video sequences encoded in H.264 and H.265 formats across five bit rates, incorporating a simulated packet loss rate (PLR) ranging from 0 to 1%.…”

mentioning

confidence: 99%