Why No Reference Metrics for Image and Video Quality Lack Accuracy and Reproducibility

Pinson, Margaret H.

doi:10.1109/tbc.2022.3191059

Cited by 10 publications

(2 citation statements)

References 87 publications

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“…The sum is calculated consistently with distortions, as in algorithm for the computation of Blind Image Quality Index [12], [15]. For comprehending and assessing vision, the measurement of natural scenes is highlighted [16]- [18]. The attribute of natural statistics is applied in NR-IQA methods, which are block-oriented, i.e.…”

Section: B Natural Scene Statistics and Metricsmentioning

confidence: 99%

Quadratic fitting model in no-reference image quality assessment

Gavrovska,

Dujković,

Samčović

et al. 2023

Telfor J

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The perceptual quality of image is affected by distortions during compression, delivery and storage. Distortions also impact automatic image quality assessment (IQA) that needs to be highly correlated with subjective scores. In the absence of reference, which is a typical scenario in practice, no-reference (NR) metrics are necessary for quality measurements. Recently such methods are proposed, and they employ natural scene statistics (NSS). The experimental analysis performed in this paper takes into consideration two fitting or regression models of several NR-IQA metrics relying on different distortion types. The results show quadratic model as promising for making relations in terms of difference mean opinion score and Shannon entropy.

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Section: B Natural Scene Statistics and Metricsmentioning

confidence: 99%

Quadratic fitting model in no-reference image quality assessment

Gavrovska,

Dujković,

Samčović

et al. 2023

Telfor J

View full text Add to dashboard Cite

show abstract

“…The methods discussed here are ML based, whereas learning free generic methods like NIQE [41] and IL-NIQE [42] also exit. The work of M H Pinson [43] draws attention towards assessment of visual media from the perspective of consumer applications. The methods like BRISQUE [29], NIQE [41], IL-NIQE [42], etc.…”

Section: Related Workmentioning

confidence: 99%

Frequency-domain blind quality assessment of blurred and blocking-artefact images using Gaussian Process Regression model

Viqar

Moinuddin

Khan

et al. 2022

Signal Processing: Image Communication

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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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Why No Reference Metrics for Image and Video Quality Lack Accuracy and Reproducibility

Cited by 10 publications

References 87 publications

Quadratic fitting model in no-reference image quality assessment

Quadratic fitting model in no-reference image quality assessment

Frequency-domain blind quality assessment of blurred and blocking-artefact images using Gaussian Process Regression model

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

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