Blind image quality assessment based on aesthetic and statistical quality-aware features

Jenadeleh, Mohsen; Masaeli, Mohammad Masood; Moghaddam, Mohsen Ebrahimi

doi:10.1117/1.jei.26.4.043018

Cited by 8 publications

(3 citation statements)

References 68 publications

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“…Based on the observation that image distortions can significantly modify the shapes of objects present in the image, Bagade et al [41] introduced shape adaptive wavelet features for NR-IQA. In contrast, Jenadeleh et al [42] boosted already existing NR-IQA features with features proposed for image aesthetics assessment. Further, they demonstrated that aesthetic aware features are able to increase the performance of perceptual quality estimation.…”

Section: Related Workmentioning

confidence: 99%

A Human Visual System Inspired No-Reference Image Quality Assessment Method Based on Local Feature Descriptors

Varga

2022

Sensors

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Objective quality assessment of natural images plays a key role in many fields related to imaging and sensor technology. Thus, this paper intends to introduce an innovative quality-aware feature extraction method for no-reference image quality assessment (NR-IQA). To be more specific, a various sequence of HVS inspired filters were applied to the color channels of an input image to enhance those statistical regularities in the image to which the human visual system is sensitive. From the obtained feature maps, the statistics of a wide range of local feature descriptors were extracted to compile quality-aware features since they treat images from the human visual system’s point of view. To prove the efficiency of the proposed method, it was compared to 16 state-of-the-art NR-IQA techniques on five large benchmark databases, i.e., CLIVE, KonIQ-10k, SPAQ, TID2013, and KADID-10k. It was demonstrated that the proposed method is superior to the state-of-the-art in terms of three different performance indices.

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Section: Related Workmentioning

confidence: 99%

A Human Visual System Inspired No-Reference Image Quality Assessment Method Based on Local Feature Descriptors

Varga

2022

Sensors

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“…The paper [7] gives the taxonomy of existing non-reference methods for assessing the quality of photographs. They can be divided into two main categories: specific, which are intended for a particular defect, and general.…”

Section: Existing Non-reference Metrics Of Image Qualitymentioning

confidence: 99%

Visualization of quality of 3D tomographic images in construction of digital rock model

Kornilov¹,

Reimers²,

Safonov³

et al. 2020

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Various types of tomography are widely employed in oil and gas industry for studying structure of rocks. Using X-ray or FIB-SEM tomography, a 3D model of a core sample is constructed for mathematical simulations of fluid flow in porous media and evaluation of physical characteristics of rock. Since images have various defects and distortions, there is a problem of selection of a fragment with the best quality from the initial 3D image. At the moment this operation is made manually on the basis of an expert's opinion and takes significant time. In this paper, we investigate applicability of existing non-reference quality metrics for evaluation of tomographic images and propose the approach for visualization of spatial change of 3D image quality. The method includes the construction of central cross-section; plotting graphs of quality and similarity measures for each slice over the cross-section; generation of combined heat map of quality of cubic fragments with various size. The proposed approach significantly accelerates and makes less subjective selection of the best region for further simulations in digital rock workflow. The choice of colour scale is considered to facilitate the analysis of graphical information for people with colour vision deficiency.

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“…Their experiments show that image integrity somewhat influences aesthetic quality scores. Jenadeleh et al [11] believe that visual quality highly correlates with the human judgment of the image quality and encompasses perceptual image qualities such as aesthetics, semantics, context, and various types of visual distortion. They proposed a schema that uses a set of aesthetics features to improve NR-IQA performance, which is confirmed by experimental results.…”

mentioning

confidence: 99%

From Technical to Aesthetics Quality Assessment and Beyond

Hosu

Saupe

Goldluecke

et al. 2020

Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends

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Every day 1.8+ billion images are being uploaded to Facebook, Instagram, Flickr, Snapchat, and WhatsApp [6]. The exponential growth of visual media has made quality assessment become increasingly important for various applications, from image acquisition, synthesis, restoration, and enhancement, to image search and retrieval, storage, and recognition. There have been two related but different classes of visual quality assessment techniques: image quality assessment (IQA) and image aesthetics assessment (IAA). As perceptual assessment tasks, subjective IQA and IAA share some common underlying factors that affect user judgments. Moreover, they are similar in methodology (especially NR-IQA in-the-wild and IAA). However, the emphasis for each is different: IQA focuses on low-level defects e.g. processing artefacts, noise, and blur, while IAA puts more emphasis on abstract and higher-level concepts that capture the subjective aesthetics experience, e.g. established photographic rules encompassing lighting, composition, and colors, and personalized factors such as personality, cultural background, age, and emotion. IQA has been studied extensively over the last decades [3, 14, 22]. There are three main types of IQA methods: full-reference (FR), reduced-reference (RR), and no-reference (NR). Among these, NR-IQA is the most challenging as it does not depend on reference images or impose strict assumptions on the distortion types and level. NR-IQA techniques can be further divided into those that predict the global image score [1, 2, 10, 17, 26] and patch-based IQA [23, 25], naming a few of the more recent approaches. Comparatively, IAA received less research attention than IQA until recently [5]. Pioneering IAA approaches adopted handcrafted features for training an aesthetics model, while earlier approaches used global and regional features [4, 12] and the later approaches employed subject-focused features [16, 24] and generic descriptors

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Blind image quality assessment based on aesthetic and statistical quality-aware features

Cited by 8 publications

References 68 publications

A Human Visual System Inspired No-Reference Image Quality Assessment Method Based on Local Feature Descriptors

A Human Visual System Inspired No-Reference Image Quality Assessment Method Based on Local Feature Descriptors

Visualization of quality of 3D tomographic images in construction of digital rock model

From Technical to Aesthetics Quality Assessment and Beyond

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