No‐reference image quality metric based on multiple deep belief networks

Alaql, Omar; Lu, Cheng‐Chang

doi:10.1049/iet-ipr.2018.5879

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…Convolutional neural networks (CNNs) have demonstrated great success in a wide range of computer vision tasks [26], [27], [28], including NR-IQA [14], [15], [16], [29]. Furthermore, pretrained CNNs can also provide a useful feature representation for a variety of tasks [30].…”

Section: Contributionsmentioning

confidence: 99%

Multi-Pooled Inception Features for No-Reference Image Quality Assessment

Varga

2020

Applied Sciences

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Image quality assessment (IQA) is an important element of a broad spectrum of applications ranging from automatic video streaming to display technology. Furthermore, the measurement of image quality requires a balanced investigation of image content and features. Our proposed approach extracts visual features by attaching global average pooling (GAP) layers to multiple Inception modules of on an ImageNet database pretrained convolutional neural network (CNN). In contrast to previous methods, we do not take patches from the input image. Instead, the input image is treated as a whole and is run through a pretrained CNN body to extract resolution-independent, multi-level deep features. As a consequence, our method can be easily generalized to any input image size and pretrained CNNs. Thus, we present a detailed parameter study with respect to the CNN base architectures and the effectiveness of different deep features. We demonstrate that our best proposal—called MultiGAP-NRIQA—is able to outperform the state-of-the-art on three benchmark IQA databases. Furthermore, these results were also confirmed in a cross database test using the LIVE In the Wild Image Quality Challenge database.

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Section: Contributionsmentioning

confidence: 99%

Multi-Pooled Inception Features for No-Reference Image Quality Assessment

Varga

2020

Applied Sciences

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“…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%