A fusion-based video quality assessment (fvqa) index

Lin, Jeng‐Feng; Liu, Tsung-Jung; Wu, Eddy Chi-Hao; Kuo, C.-C. Jay

doi:10.1109/apsipa.2014.7041705

Cited by 53 publications

(28 citation statements)

References 17 publications

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“…A metric called Video Multimethod Assessment Fusion (VMAF) for the approximation of perceptual quality, which combines multiple methods by machine learning, gained attention recently [38][39][40]. Also, subjective tests can be conducted to assess the perceptual quality.…”

Section: C) Metricsmentioning

confidence: 99%

A Comprehensive Video Codec Comparison

Laude

Adhisantoso

Voges

et al. 2019

SIP

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In this paper, we compare the video codecs AV1 (version 1.0.0-2242 from August 2019), HEVC (HM and x265), AVC (x264), the exploration software JEM which is based on HEVC, and the VVC (successor of HEVC) test model VTM (version 4.0 from February 2019) under two fair and balanced configurations: All Intra for the assessment of intra coding and Maximum Coding Efficiency with all codecs being tuned for their best coding efficiency settings. VTM achieves the highest coding efficiency in both configurations, followed by JEM and AV1. The worst coding efficiency is achieved by x264 and x265, even in the placebo preset for highest coding efficiency. AV1 gained a lot in terms of coding efficiency compared to previous versions and now outperforms HM by 24% BD-Rate gains. VTM gains 5% over AV1 in terms of BD-Rates. By reporting separate numbers for JVET and AOM test sequences, it is ensured that no bias in the test sequences exists. When comparing only intra coding tools, it is observed that the complexity increases exponentially for linearly increasing coding efficiency.

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Section: C) Metricsmentioning

confidence: 99%

A Comprehensive Video Codec Comparison

Laude

Adhisantoso

Voges

et al. 2019

SIP

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“…These features are fused using a supervised learning regression model to provide a single output result called VMAF score. This score ranges from 0 to 100 per video frame, 100 being the quality of a video identical to the reference [16]. As presented in the next section, VMAF is attracting a lot of attention both in academic research and in industrial initiatives nowadays.…”

Section: State-of-the-artmentioning

confidence: 99%

Practical Evaluation of VMAF Perceptual Video Quality for WebRTC Applications

et al. 2019

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WebRTC is the umbrella term for several emergent technologies aimed to exchange real-time media in the Web. Like other media-related services, the perceived quality of WebRTC communication can be measured using Quality of Experience (QoE) indicators. QoE assessment methods can be classified as subjective (users’ evaluation scores) or objective (models computed as a function of different parameters). In this paper, we focus on VMAF (Video Multi-method Assessment Fusion), which is an emergent full-reference objective video quality assessment model developed by Netflix. VMAF is typically used to assess video streaming services. This paper evaluates the use of VMAF in a different type of application: WebRTC. To that aim, we present a practical use case built on the top of well-known open source technologies, such as JUnit, Selenium, Docker, and FFmpeg. In addition to VMAF, we also calculate other objective QoE video metrics such as Visual Information Fidelity in the pixel domain (VIFp), Structural Similarity (SSIM), or Peak Signal-to-Noise Ratio (PSNR) applied to a WebRTC communication in different network conditions in terms of packet loss. Finally, we compare these objective results with a subjective evaluation using a Mean Opinion Score (MOS) scale to the same WebRTC streams. As a result, we found a strong correlation of the subjective video quality perceived in WebRTC video calls with the objective results computed with VMAF and VIFp in comparison with SSIM and PSNR and their variants.

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“…Expanding on previous work by Liu et al [29] and Lin et al [30], researchers developed the Video Multimethod Assessment Fusion (VMAF) [5] FR VQA metric that employs a machine learning approach in order to map multiple elementary objective quality metrics to subjective quality ratings (MOS). The rationale behind this approach is that although each individual objective metric cannot fully capture the perceptual quality of the video, as it has its respective drawbacks and advantages, "fusing" multiple metrics together by assigning weights to each through a machine learning algorithm could potentially preserve the advantages of each metric and deliver a more accurate final video quality score.…”

Section: The Video Multimethods Assessment Fusion (Vmaf) Metricmentioning

confidence: 99%