Framework for optimal region of interest–based quality assessment in wireless imaging

Engelke, Ulrich; Zepernick, Hans‐Jürgen

doi:10.1117/1.3267097

Cited by 24 publications

(23 citation statements)

References 38 publications

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“…Here, the mean location and mean dimension of all ROI selections were determined by averaging, respectively, the ROI center coordinates and the ROI dimensions in both horizontal and vertical direction. The ROI experiment is explained and analysed in detail in [4].…”

Section: Region-of-interest Selectionmentioning

confidence: 99%

Visual fixation patterns in subjective quality assessment: The relative impact of image content and structural distortions

Engelke

Zepernick

Maeder

2010

2010 International Symposium on Intelligent Signal Processing and Communication Systems

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Section: Region-of-interest Selectionmentioning

confidence: 99%

Visual fixation patterns in subjective quality assessment: The relative impact of image content and structural distortions

Engelke

Zepernick

Maeder

2010

2010 International Symposium on Intelligent Signal Processing and Communication Systems

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“…Models on VA are often developed and validated by visual fixation patterns through eye tracking experiments [16,17]. In [18] a framework is proposed in order to extend existing image quality metrics with a simple VA model. A subjective region of interest (ROI) experiment was performed, with seven images, in which the viewers' task was to select within each image the region that drew most of their attention.…”

Section: Proposed Metricmentioning

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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“…More recently, however, there have been several studies in which spatial saliency has been incorporated into still image quality metrics to improve quality prediction performance [2][3][4][5]. However, there has been only little efforts to incorporate visual saliency into video quality metrics (VQM).…”

Section: Introductionmentioning

confidence: 99%

Modelling saliency awareness for objective video quality assessment

Engelke

Barkowsky

Callet

et al. 2010

2010 Second International Workshop on Quality of Multimedia Experience (QoMEX)

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Existing video quality metrics do usually not take into consideration that spatial regions in video frames are of varying saliency and thus, differently attract the viewer's attention. This paper proposes a model of saliency awareness to complement existing video quality metrics, with the aim to improve the agreement of objectively predicted quality with subjectively rated quality. For this purpose, we conducted a subjective experiment in which human observers rated the annoyance of videos with transmission distortions appearing either in a salient region or in a non-salient region. The mean opinion scores confirm that distortions in salient regions are perceived much more annoying. It is shown that application of the saliency awareness model to two video quality metrics considerably improves their quality prediction performance.Index Terms-Video quality metrics, visual saliency, eye tracking, subjective quality experiment.

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Framework for optimal region of interest–based quality assessment in wireless imaging

Abstract: Abstract. Images usually exhibit regions that

Cited by 24 publications

References 38 publications

Visual fixation patterns in subjective quality assessment: The relative impact of image content and structural distortions

Visual fixation patterns in subjective quality assessment: The relative impact of image content and structural distortions

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

Modelling saliency awareness for objective video quality assessment

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