AVT-VQDB-UHD-1: A Large Scale Video Quality Database for UHD-1

Rao, Rakesh Rao Ramachandra; Göring, Steve; Robitza, Werner; Feiten, Bernhard; Raake, Alexander

doi:10.1109/ism46123.2019.00012

Cited by 36 publications

(31 citation statements)

References 26 publications

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“…Specif-ically, VMAF per frame is averaged over all frames, as a default ("mean"). Therefore, there is one drawback: the VMAF algorithm cannot be used for evaluating the impact of framerate reduction on the quality [38] because the absolute average pixel difference is only used as a temporal feature.…”

Section: Video Multimethods Assessment Fusion: Vmafmentioning

confidence: 99%

“…using a mapping function (hereafter, converted VMAF is called MOSfromVMAF) because the VMAF algorithm calculates quality per frame ranging from 0 to 100, where coefficients of the mapping function are derived per framerate. This is because the VMAF algorithm cannot evaluate the impact of framerate reduction on the quality [38] and does not support the quality estimation of SP with 4K-UHD resolution and 60 fps at 5-7 H viewing distance (i.e., H indicates the display height). Once coefficients of a mapping function are derived, users of the derivation procedure do not need to conduct a subjective assessment test for deriving coefficients of a mapping function and can use the coefficients described in Sect.…”

Section: Derivation Procedures Of Coefficients Of Metadata-based Modelsmentioning

confidence: 99%

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Derivation Procedure of Coefficients of Metadata-Based Model for Adaptive Bitrate Streaming Services

Yamagishi

Egi

Yoshimura

et al. 2021

IEICE Trans. Commun.

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Since the quality of video streaming services is degraded due to the encoding, network congestion, and lack of a playout buffer, the normality of services needs to be monitored by gathering the quality measured at the end clients. When measuring quality at the end clients, the computational power should be sufficiently low, the bitstream information cannot be accessed for the quality estimation due to the encryption, and reference video cannot be used at the end clients. Therefore, metadata-based models have been developed and standardized that take metadata such as the resolution, framerate, and bitrate, and stalling information as input and calculate the quality. However, calculated quality for linear TV and video on demand (VoD) services cannot be compared because metadata-based models cannot calculate the impacts of codec strategies (e.g., H.264/AVC, H.265/HEVC, and AV1) and configurations (e.g., 1-pass encoding for linear TV or 2-pass encoding for VoD) on the quality. To take into account the impact of codec strategies and configurations, coefficients of metadatabased model need to be optimized per codec strategy and configuration using subjective quality. However, extensive subjective assessment tests are difficult to frequently conduct because they are expensive and time consuming and need to be conducted by video quality experts. Therefore, to monitor the quality of several types of video streaming services (e.g., linear TV and VoD) and to compare these qualities, a derivation procedure is proposed for obtaining coefficients of metadata-based models using a fullreference model. To validate the procedure, extensive subjective assessment tests were conducted. Finally, it is shown that three metadata-based models (i.e., the P.1203.1 mode 0 model, extended P.1203.1 mode 0 model, and model proposed by Yamagishi et al.) based on the proposed procedure using the video multimethod assessment fusion (VMAF) algorithm estimate quality accurately in terms of root mean squared error.

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Section: Video Multimethods Assessment Fusion: Vmafmentioning

confidence: 99%

Section: Derivation Procedures Of Coefficients Of Metadata-based Modelsmentioning

confidence: 99%

Derivation Procedure of Coefficients of Metadata-Based Model for Adaptive Bitrate Streaming Services

Yamagishi

Egi

Yoshimura

et al. 2021

IEICE Trans. Commun.

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“…Accordingly, at its core, "hyfu" is a random forest (RF) based model. The model has been trained on four databases and validated independently on the four tests of the AVT-VQDB-UHD-1 database [40]. The application scope of "hyfu" is the same as ITU-T Rec.…”

Section: B Hybrid Modelsmentioning

confidence: 99%

“…AVT-VQBD-UHD-1 [40] is a publicly available dataset 8 created by the authors' group. Like the AVT-PNATS-UHD-1 dataset, this dataset also consists of four different subjective tests.…”

Section: Avt-vqdb-uhd-1mentioning

confidence: 99%

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

2022

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Video expert assessment of high quality video for Video Assistant Referee (VAR): A comparative study

Brunnström,

Djupsjöbacka,

Billingham

et al. 2023

Multimed Tools Appl

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The International Football Association Board decided to introduce Video Assistant Referee (VAR) in 2018. This led to the need to develop methods for quality control of the VAR-systems. This article focuses on the important aspect to evaluate the video quality. Video Quality assessment has matured in the sense that there are standardized, commercial products and established open-source solutions to measure it with objective methods. Previous research has primarily focused on the end-user quality assessment. How to assess the video in the contribution phase of the chain is less studied. The novelties of this study are two-fold: 1) The user study is specifically targeting video experts i.e., to assess the perceived quality of video professionals working with video production. 2) Six video quality models have been independently benchmarked against the user data and evaluated to show which of the models could provide the best predictions of perceived quality. The independent evaluation is important to get unbiased results as shown by the Video Quality Experts Group. An experiment was performed involving 25 video experts in which they rated the perceived quality. The video formats tested were High-Definition TV both progressive and interlaced as well as a quarters size format that was scaled down half the size in both width and height. The videos were encoded with both H.264 and Motion JPEG for the full size but only H.264 for the quarter size. Bitrates ranged from 80 Mbit/s down to 10 Mbit/s. We could see that for H.264 that the quality was overall very good but dropped somewhat for 10 Mbit/s. For Motion JPEG the quality dropped over the whole range. For the interlaced format the degradation that was based on a simple deinterlacing method did receive overall low ratings. For the quarter size three different scaling algorithms were evaluated. Lanczos performed the best and Bilinear the worst. The performance of six different video quality models were evaluated for 1080p and 1080i. The Video Quality Metric for Variable Frame Delay had the best performance for both formats, followed by Video Multimethod Assessment Fusion method and the Video Quality Metric General model.

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AVT-VQDB-UHD-1: A Large Scale Video Quality Database for UHD-1

Cited by 36 publications

References 26 publications

Derivation Procedure of Coefficients of Metadata-Based Model for Adaptive Bitrate Streaming Services

Derivation Procedure of Coefficients of Metadata-Based Model for Adaptive Bitrate Streaming Services

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

Video expert assessment of high quality video for Video Assistant Referee (VAR): A comparative study

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