A strategy to jointly test image quality estimators subjectively

Reibman, Amy R.

doi:10.1109/icip.2012.6467156

Cited by 10 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this is difficult because we do not know the viewers' preferences a-priori. In consequence, we use a method similar to that described in [14] to select the images. According to this approach, for a given high resolution test image, we select a corresponding low resolution test image which divides a set of QEs into two groups, with approximately half of the QEs classifying an image pair one way and approximately half classifying the pair the other way.…”

Section: Test Set Creationmentioning

confidence: 99%

Image quality estimation for different spatial resolutions

Demirtas

Reibman

Jafarkhani

2013

2013 IEEE International Conference on Image Processing

Self Cite

View full text Add to dashboard Cite

State of the art visual quality assessment methods are effective when the input image and the reference image have the same resolution. However, estimating the quality of an image that has spatial resolution different than that of the reference image is still a challenging problem. In this work, we design a quality estimator (QE) to estimate the quality of a distorted image with a lower resolution compared to the reference image. We also present a subjective test environment to explore the success of the proposed algorithm in comparison with other QEs. The subjective tests demonstrate that the proposed method works better than other approaches.

show abstract

Section: Test Set Creationmentioning

confidence: 99%

Image quality estimation for different spatial resolutions

Demirtas

Reibman

Jafarkhani

2013

2013 IEEE International Conference on Image Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, recent work examined the adaptive fusion of multiple objective quality metrics according to distortion types [19]. The efficacy of objective metrics in predicting the visual quality degradation incurred by certain distortion types has also been studied by Reibman, Barkowsky, Vu et al [20], [21], [22], [23], as well as by the ITU-T VQEG/JEG committees and others [24], [25]. These important results provide insight on the influence of certain types of degradations in the visual quality of images and video and may help in devising new objective metrics for visual quality assessment.…”

Section: B Related Work and Paper Contributionmentioning

confidence: 99%

Perceptual video quality estimation by regression with myopic experts

Giotsas

Deligiannis

Fisher

et al. 2015

2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX)

View full text Add to dashboard Cite

Objective video quality metrics can be viewed as "myopic" expert systems that focus on particular aspects of visual information in video, such as image edges or motion parameters. We conjecture that the combination of many such high-level metrics leads to statistically-significant improvement in the prediction of reference-based perceptual video quality in comparison to each individual metric. To examine this hypothesis in a systematic and rigorous manner, we use: (i) the LIVE and the EPFL/PoliMi databases that provide the difference mean opinion scores (DMOS) for several video sequences under encoding and packet-loss errors; (ii) ten well-known metrics that range from mean-squared error based criteria to sophisticated visual quality estimators; (iii) five variants of regression-based supervised learning. For 400 experimental trials with random (non-overlapping) estimation and prediction subsets taken from both databases, we show that the best of our regression methods: (i) leads to statistically-significant improvement against the best individual metrics for DMOS prediction for more than 97% of the experimental trials; (ii) is statistically-equivalent to the performance of humans rating the video quality for 36.75% of the experiments with the EPFL/PoliMi database. On the contrary, no single metric achieves such statistical equivalence to human raters in any of the experimental trials.

show abstract

“…On the other hand, cross-content comparisons probe properties and relations varying across contents, e.g., attributes such as colourfulness [9]. The use of cross-content comparisons to assess the quality of processing or compression algorithms has been little studied in the literature, e.g., to relate rankings from different contents [10], to find the accuracy of quality estimators [11], and to determine how it can benefit the fusion of different quality scales induced by per content PWC experiments [4], [12]. These studies have brought evidence that cross-content pairs in PWC can align quality scores from different contents on the same scale, and increase the accuracy of psychometric scaling.…”

Section: Introductionmentioning

confidence: 99%

Analysing the Impact of Cross-Content Pairs on Pairwise Comparison Scaling

Zerman

Valenzise

Smolić

2019

2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX)

View full text Add to dashboard Cite

Pairwise comparisons (PWC) methodology is one of the most commonly used methods for subjective quality assessment, especially for computer graphics and multimedia applications. Unlike rating methods, a psychometric scaling operation is required to convert PWC results to numerical subjective quality values. Due to the nature of this scaling operation, the obtained quality scores are relative to the set they are computed in. While it is customary to compare different versions of the same content, in this work we study how crosscontent comparisons may benefit psychometric scaling. For this purpose, we use two different video quality databases which have both rating and PWC experiment results. The results show that despite same-content comparisons play a major role in the accuracy of psychometric scaling, the use of a small portion of cross-content comparison pairs is indeed beneficial to obtain more accurate quality estimates.

show abstract

A strategy to jointly test image quality estimators subjectively

Cited by 10 publications

References 13 publications

Image quality estimation for different spatial resolutions

Image quality estimation for different spatial resolutions

Perceptual video quality estimation by regression with myopic experts

Analysing the Impact of Cross-Content Pairs on Pairwise Comparison Scaling

Contact Info

Product

Resources

About