Impact of subjective dataset on the performance of image quality metrics

Tourancheau, Sylvain; Autrusseau, Florent; Sazzad, Z. M. Parvez; Horita, Yuukou

doi:10.1109/icip.2008.4711767

Cited by 45 publications

(25 citation statements)

References 5 publications

(3 reference statements)

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“…Below: residuals for the linear approximation and norm of residuals. Table 5 Correlation coefficient versus MOS, IRCCyN/IVC image database [24] PSNR RR [4] Proposed RR MSSIM C4 [13,29] [4]. Above: scatter plot between mean opinion score and metric in [4].…”

Section: Simulation Set-up and Resultsmentioning

confidence: 99%

A reduced-reference perceptual image and video quality metric based on edge preservation

Martini

Villarini

Fiorucci

2012

EURASIP J. Adv. Signal Process.

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In image and video compression and transmission, it is important to rely on an objective image/video quality metric which accurately represents the subjective quality of processed images and video sequences. In some scenarios, it is also important to evaluate the quality of the received video sequence with minimal reference to the transmitted one. For instance, for quality improvement of video transmission through closed-loop optimisation, the video quality measure can be evaluated at the receiver and provided as feedback information to the system controller. The original image/video sequence-prior to compression and transmission-is not usually available at the receiver side, and it is important to rely at the receiver side on an objective video quality metric that does not need reference or needs minimal reference to the original video sequence. The observation that the human eye is very sensitive to edge and contour information of an image underpins the proposal of our reduced reference (RR) quality metric, which compares edge information between the distorted and the original image. Results highlight that the metric correlates well with subjective observations, also in comparison with commonly used full-reference metrics and with a state-of-the-art RR metric.

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Section: Simulation Set-up and Resultsmentioning

confidence: 99%

A reduced-reference perceptual image and video quality metric based on edge preservation

Martini

Villarini

Fiorucci

2012

EURASIP J. Adv. Signal Process.

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“…Since almost all common databases contain a significant proportion of images taken from the Kodak Lossless True Color Image Suite, it is reasonable to suggest that these pictures have borne a significant influence on the IQA field. Conversely, it is both intuitive and supported by empirical evidence to suggest that reliable results may be obtained by using image databases that are large and diverse [25].…”

Section: Introductionmentioning

confidence: 98%

Influence of affective image content on subjective quality assessment

Linde

Doe

2012

J. Opt. Soc. Am. A

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Image quality assessment (IQA) enables distortions introduced into an image (e.g., through lossy compression or broadcast) to be measured and evaluated for severity. It is unclear to what degree affective image content may influence this process. In this study, participants (n 25) were found to be unable to disentangle affective image content from objective image quality in a standard IQA procedure (single stimulus numerical categorical scale). We propose that this issue is worthy of consideration, particularly in single stimulus IQA techniques, in which a small number of handpicked images, not necessarily representative of the gamut of affect seen in true broadcasting, and unrated for affective content, serve as stimuli.

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“…It is noted in [38] that the performance of many image quality metrics could be quite different across databases. The difference in performance can be attributed to the differences in quality range, distortions, and contents across databases.…”

Section: Figurementioning

confidence: 99%

A no-reference objective image quality metric based on perceptually weighted local noise

Zhu

Karam

2014

J Image Video Proc

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This work proposes a perceptual based no-reference objective image quality metric by integrating perceptually weighted local noise into a probability summation model. Unlike existing objective metrics, the proposed no-reference metric is able to predict the relative amount of noise perceived in images with different content, without a reference. Results are reported on both the LIVE and TID2008 databases. The proposed no-reference metric achieves consistently a good performance across noise types and across databases as compared to many of the best very recent no-reference quality metrics. The proposed metric is able to predict with high accuracy the relative amount of perceived noise in images of different content.

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Impact of subjective dataset on the performance of image quality metrics

Cited by 45 publications

References 5 publications

A reduced-reference perceptual image and video quality metric based on edge preservation

A reduced-reference perceptual image and video quality metric based on edge preservation

Influence of affective image content on subjective quality assessment

A no-reference objective image quality metric based on perceptually weighted local noise

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