FQI: feature‐based reduced‐reference image quality assessment method for screen content images

Rahul, Kumar; Tiwari, Anil Kumar

doi:10.1049/iet-ipr.2018.5496

Cited by 20 publications

(16 citation statements)

References 44 publications

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“…These are Spearman rank‐order correlation coefficient (SC), Pearson linear correlation coefficient (PC) and root mean squared error (RE). These evaluation criteria are chosen based on the common practice of the existing works in the literature [5–11, 14–20, 22–24, 26, 28].…”

Section: Resultsmentioning

confidence: 99%

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A generalized quality assessment method for natural and screen content images

Loh

Bong

2020

IET Image Processing

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A generalized objective quality assessment method is proposed for natural images and screen content images. Since natural images and screen content images have different statistical properties, the modelling of a generalized quality assessment method that works for both types of images is complicated because some properties of natural images and screen content images are conflicting to one another. The proposed method assesses the perceptual quality of an image based on edge magnitude and direction. In this method, an image is first separated into regions with high and low gradients. Gradient is used due to the small perceptual span of the human visual system for textual content. For high gradient regions, small kernel size of Prewitt operators is used to obtain the gradient magnitude and direction. Correspondingly, bigger kernel size of Prewitt operators is utilized for low gradient regions. Visual quality indices are computed from both regions and pooled to obtain the final quality index. From the performance comparison, it is shown that the proposed method could assess the perceived quality of natural images and screen content images with high accuracy.

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Section: Resultsmentioning

confidence: 99%

“…There are also methods that are not based on edge property. Feature quality index (FQI) [28], proposed by Rahul and Tiwari, is based on assessing the feature points. The underlying motivation is that the HVS is more sensitive towards the changes of features instead of intensity.…”

Section: Introductionmentioning

confidence: 99%

A generalized quality assessment method for natural and screen content images

Loh

Bong

2020

IET Image Processing

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“…The second type predicts perceptual quality by modelling the characteristics of the human visual system (HVS) from psychological vision science. These approaches quantify the visual attributes in image understanding and appreciation by making assumption of the HVS's behaviour [5][6][7][8]. The third type trains a deep neural network to predict image quality based on a corpus of high-level features [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A blind contour‐aware quality model for sonar images

Chen

Lin

Zhang

et al. 2021

IET Image Processing

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Since the penetration depth of visible light is limited in muddy and dark deep marine environment, sonar imaging, which is independent of natural light, has been attracting much attention in underwater detections. However, the visual quality of sonar image is inevitably damaged during the acquisition and the transmission in the sophisticated and dynamic underwater environment. The quality decrease can further bring negative impacts on oceanic information analysis. To measure the quality decrease of sonar images, this paper proposes a contour-aware model for sonar image quality assessment (CaMSIQA). The CaMSIQA metric is established upon the conclusion that the quality of sonar images can be defined as a measure of their utilities in real application scenarios. We exploit a contour information statistic (CIS) model that is critical to object recognition of sonar images here. Quantifying the changes of the CIS model between the unimpaired and test sonar images makes it possible to perceive the quality decrease of sonar images. Extensive experimental results have demonstrated the effectiveness of the proposed method compared with current mainstream image quality assessment (IQA) methods without full reference information.

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“…These image quality measures provide a common comparison index to indicate the best image processing system in noise removal, image enhancement, and recolouring applications. In the last two decades, many existing image quality assessments (IQAs) have been published [3–5]. Among all these IQAs, no‐reference (blind) image quality algorithms (BIQAs) are suitable for real‐world applications [6–9], since the distortion‐free reference image, which does not always exist, is not required.…”

Section: Introductionmentioning

confidence: 99%