Study of the Subjective Visibility of Packet Loss Artifacts in Decoded Video Sequences

Korhonen, Jari

doi:10.1109/tbc.2018.2832465

Cited by 16 publications

(7 citation statements)

References 51 publications

(67 reference statements)

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“…Some works have evaluated the effects of packet losses in H.264/AVC and H.265/HEVC video transmission. The authors from [14] used H.264/AVC to present a subjective video degradation analysis under packet loss scenarios whereas differentiating the impact of reference and non-reference pictures. In [15], an encoding-time technique is presented for defining intra-predicted frames (i.e., IRAP) positioning in the H.264/AVC bitstream to improve packet loss resiliency.…”

Section: Background and Related Workmentioning

confidence: 99%

Improving Content-Aware Video Streaming in Congested Networks with In-Network Computing

Gobatto¹,

Saquetti²,

Diniz³

et al. 2022

Preprint

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Network congestion and packet loss pose an everincreasing challenge to video streaming. Despite the research efforts toward making video encoding schemes resilient to lossy network conditions, forwarding devices have not considered monitoring packet content to prioritize packets and minimize the impact of packet loss on video transmission. In this work, we advocate in favor of in-network computing employing a packet drop algorithm and an in-network hardware module to devise a solution for improving content-aware video streaming in congested network. Results show that our approach can reduce intra-predicted packet loss by over 80% at negligible resource usage and performance costs.

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Section: Background and Related Workmentioning

confidence: 99%

Improving Content-Aware Video Streaming in Congested Networks with In-Network Computing

Gobatto¹,

Saquetti²,

Diniz³

et al. 2022

Preprint

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show abstract

“…Choi et al [20] studied the relationship between temporal visual masking and subjective perception quality for local flicker in compressed videos. Korhonen et al [21] observed the effect of packet loss and subjective perception in decoded videos. Multi-distortion quality evaluation methods are based on Natural Scene Statistic (NSS) [22].…”

Section: Related Workmentioning

confidence: 99%

“…Reference [7] utilized Gaussian scale mixture model to analyze the conditional entropy of reference image and distorted image, and constructed RR-VQA method on spatial scale. Although, the advantage of NR-VQA without any reference information is of great application value [8]- [10], most of the existing NR-VQA methods have the limitation of artificially extracting features, and are difficult to obtain ideal generalization performance.…”

Section: Introductionmentioning

confidence: 99%

Deep Quality Assessment of Compressed Videos: A Subjective and Objective Study

Lin¹,

Wang²,

He³

et al. 2022

Preprint

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In the video coding process, the perceived quality of a compressed video is evaluated by full-reference quality evaluation metrics. However, it is difficult to obtain reference videos with perfect quality. To solve this problem, it is critical to design no-reference compressed video quality assessment algorithms, which assists in measuring the quality of experience on the server side and resource allocation on the network side. Convolutional Neural Network (CNN) has shown its advantage in Video Quality Assessment (VQA) with promising successes in recent years. A large-scale quality database is very important for learning accurate and powerful compressed video quality metrics. In this work, a semi-automatic labeling method is adopted to build a large-scale compressed video quality database, which allows us to label a large number of compressed videos with manageable human workload. The resulting Compressed Video quality database with Semi-Automatic Ratings (CVSAR), so far the largest of compressed video quality database. We train a no-reference compressed video quality assessment model with a 3D CNN for SpatioTemporal Feature Extraction and Evaluation (STFEE). Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics and achieves promising generalization performance in cross-database tests. The CVSAR database and STFEE model will be made publicly available to facilitate reproducible research.

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“…The paper [ 22 ] examined the perceptibility of packet loss deficiency in the scenes’ temporal and spatial sections. End users tapped the touchscreen while the video was streamed, in the locations of artifacts.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Reservation of Network Resources According to Video Classification Scenes

Sevcik

Vozňák

2021

Sensors

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Video quality evaluation needs a combined approach that includes subjective and objective metrics, testing, and monitoring of the network. This paper deals with the novel approach of mapping quality of service (QoS) to quality of experience (QoE) using QoE metrics to determine user satisfaction limits, and applying QoS tools to provide the minimum QoE expected by users. Our aim was to connect objective estimations of video quality with the subjective estimations. A comprehensive tool for the estimation of the subjective evaluation is proposed. This new idea is based on the evaluation and marking of video sequences using the sentinel flag derived from spatial information (SI) and temporal information (TI) in individual video frames. The authors of this paper created a video database for quality evaluation, and derived SI and TI from each video sequence for classifying the scenes. Video scenes from the database were evaluated by objective and subjective assessment. Based on the results, a new model for prediction of subjective quality is defined and presented in this paper. This quality is predicted using an artificial neural network based on the objective evaluation and the type of video sequences defined by qualitative parameters such as resolution, compression standard, and bitstream. Furthermore, the authors created an optimum mapping function to define the threshold for the variable bitrate setting based on the flag in the video, determining the type of scene in the proposed model. This function allows one to allocate a bitrate dynamically for a particular segment of the scene and maintains the desired quality. Our proposed model can help video service providers with the increasing the comfort of the end users. The variable bitstream ensures consistent video quality and customer satisfaction, while network resources are used effectively. The proposed model can also predict the appropriate bitrate based on the required quality of video sequences, defined using either objective or subjective assessment.

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Study of the Subjective Visibility of Packet Loss Artifacts in Decoded Video Sequences

Cited by 16 publications

References 51 publications

Improving Content-Aware Video Streaming in Congested Networks with In-Network Computing

Improving Content-Aware Video Streaming in Congested Networks with In-Network Computing

Deep Quality Assessment of Compressed Videos: A Subjective and Objective Study

Adaptive Reservation of Network Resources According to Video Classification Scenes

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