AVQBits—Adaptive Video Quality Model Based on Bitstream Information for Various Video Applications

Rao, Rakesh Rao Ramachandra; Göring, Steve; Raake, Alexander

doi:10.1109/access.2022.3195527

Cited by 13 publications

(4 citation statements)

References 94 publications

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“…Considering the spherical characteristics of 360 • videos, specialized metrics have been introduced, including sphere PSNR, Weighted to Spherically PSNR [90], and multiscale weighted-sphere uniform SSIM [91], among others. Dziembowski et al [92] introduced a novel objective quality metric called immersive video PSNR, which incorporates two techniques for assessing quality loss in common immersive video distortions: the corresponding pixel shift and the global component difference.…”

Section: Qoe Assessment and Predictionmentioning

confidence: 99%

“…The MOS was estimated for a catalog of 25 different videos, and the model's generalization performance across the entire catalog was evaluated by training on the shortest video session, resulting in an average root mean square error of 1.05 for MOS estimation. Rao et al [90] introduced a versatile, bitstream-based video quality model that is applicable in various contexts, including video service monitoring, video encoding quality evaluation, gaming video QoE assessment, and omnidirectional video quality evaluation. Dinaki et al [98] used indicators such as playtime, average buffering length, buffering frequency, average bitrate, and happiness score to predict video QoE before displaying the effects on the client's screen.…”

Section: Qoe Assessment and Predictionmentioning

confidence: 99%

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A Survey on Video Streaming for Next-Generation Vehicular Networks

Huang,

Cheng,

Lien

et al. 2024

Electronics

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As assisted driving technology advances and vehicle entertainment systems rapidly develop, future vehicles will become mobile cinemas, where passengers can use various multimedia applications in the car. In recent years, the progress in multimedia technology has given rise to immersive video experiences. In addition to conventional 2D videos, 360° videos are gaining popularity, and volumetric videos, which can offer users a better immersive experience, have been discussed. However, these applications place high demands on network capabilities, leading to a dependence on next-generation wireless communication technology to address network bottlenecks. Therefore, this study provides an exhaustive overview of the latest advancements in video streaming over vehicular networks. First, we introduce related work and background knowledge, and provide an overview of recent developments in vehicular networking and video types. Next, we detail various video processing technologies, including the latest released standards. Detailed explanations are provided for network strategies and wireless communication technologies that can optimize video transmission in vehicular networks, paying special attention to the relevant literature regarding the current development of 6G technology that is applied to vehicle communication. Finally, we proposed future research directions and challenges. Building upon the technologies introduced in this paper and considering diverse applications, we suggest a suitable vehicular network architecture for next-generation video transmission.

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Section: Qoe Assessment and Predictionmentioning

confidence: 99%

Section: Qoe Assessment and Predictionmentioning

confidence: 99%

A Survey on Video Streaming for Next-Generation Vehicular Networks

Huang,

Cheng,

Lien

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…An attempt to develop a somewhat unified model for VQA can be found in [40], where the authors develop different instances of the AVQBits algorithm. The authors claim that the algorithm can be used to assess video quality in different contexts, such as video service monitoring, evaluation of video encoding quality, gaming video QoE, and omnidirectional video quality.…”

Section: Related Workmentioning

confidence: 99%

“…SROCC PCC PSNR 0.695 0.6685 SSIM [16] 0.4494 0.4526 MS-SSIM [17] 0.4898 0.4673 FSIM [18] 0.5251 0.5008 ST-RRED [19] 0.5531 0.5107 SpEED [20] 0.4861 0.4449 FRQM [50] 0.4216 0.452 VMAF [51] 0.7303 0.7071 DeepVQA [32] 0.3463 0.3329 GSTI [13] 0.7909 0.791 AVQBits|M3 [40] 0.7118 0.7805 AVQBits|M1 [40] 0.4809 0.5528 AVQBits|M0 [40] 0.4947 0.5538 AVQBits|H0|s [40] 0.7324 0.7887 AVQBits|H0|f [40] 0.674 0.7242 FLAME-VQA 0.8961 0.9086…”

Section: Model Namementioning

confidence: 99%

FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment

Mrvelj,

Matulin

2023

Future Internet

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In the quest to optimize user experience, network, and service, providers continually seek to deliver high-quality content tailored to individual preferences. However, predicting user perception of quality remains a challenging task, given the subjective nature of human perception and the plethora of technical attributes that contribute to the overall viewing experience. Thus, we introduce a Fuzzy Logic-bAsed ModEl for Video Quality Assessment (FLAME-VQA), leveraging the LIVE-YT-HFR database containing 480 video sequences and subjective ratings of their quality from 85 test subjects. The proposed model addresses the challenges of assessing user perception by capturing the intricacies of individual preferences and video attributes using fuzzy logic. It operates with four input parameters: video frame rate, compression rate, and spatio-temporal information. The Spearman Rank–Order Correlation Coefficient (SROCC) and Pearson Correlation Coefficient (PCC) show a high correlation between the output and the ground truth. For the training, test, and complete dataset, SROCC equals 0.8977, 0.8455, and 0.8961, respectively, while PCC equals 0.9096, 0.8632, and 0.9086, respectively. The model outperforms comparative models tested on the same dataset.

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No-reference video quality assessment from artifacts and content characteristics: a neuro-fuzzy framework for video quality evaluation

Ghosh,

Mahapatra

2023

Multimed Tools Appl

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AVQBits—Adaptive Video Quality Model Based on Bitstream Information for Various Video Applications

Abstract: We acknowledge the Open Access Publication Fund of the Technische Universität Ilmenau for support in covering the publication costs.

Cited by 13 publications

References 94 publications

A Survey on Video Streaming for Next-Generation Vehicular Networks

A Survey on Video Streaming for Next-Generation Vehicular Networks

FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment

No-reference video quality assessment from artifacts and content characteristics: a neuro-fuzzy framework for video quality evaluation

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