Disregarding the Big Picture: Towards Local Image Quality Assessment

Wiedemann, Oliver; Hosu, Vlad; Saupe, Dietmar

doi:10.1109/qomex.2018.8463384

Cited by 14 publications

(8 citation statements)

References 17 publications

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“…We hope our approach will enable the scientific community to design better and larger databases in the future. Moreover, our dataset KonIQ-10k already has facilitated the design of new blind IQA methods using deep learning [24,25].…”

Section: Discussionmentioning

confidence: 99%

KonIQ-10k: Towards an ecologically valid and large-scale IQA database

Lin,

Hosu,

Saupe

2018

Preprint

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The main challenge in applying state-of-the-art deep learning methods to predict image quality in-the-wild is the relatively small size of existing quality scored datasets. The reason for the lack of larger datasets is the massive resources required in generating diverse and publishable content. We present a new systematic and scalable approach to create large-scale, authentic and diverse image datasets for Image Quality Assessment (IQA). We show how we built an IQA database, KonIQ-10k 1 , consisting of 10,073 images, on which we performed very large scale crowdsourcing experiments in order to obtain reliable quality ratings from 1,467 crowd workers (1.2 million ratings). We argue for its ecological validity by analyzing the diversity of the dataset, by comparing it to stateof-the-art IQA databases, and by checking the reliability of our user studies.

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Section: Discussionmentioning

confidence: 99%

KonIQ-10k: Towards an ecologically valid and large-scale IQA database

Lin,

Hosu,

Saupe

2018

Preprint

Self Cite

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“…The main idea is to convert each 4K square stimulus into 4 patches with a square size of 1080p each. In addition to solving the presentation dilemma, such an approach will also enable the possibility to analyse the connection between patch-based and overall image quality considering patches with higher-resolution, in contrast to [114], where only lower resolution images are used.…”

Section: Approach and Challengesmentioning

confidence: 99%

Quality assessment of higher resolution images and videos with remote testing

2023

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In many research fields, human-annotated data plays an important role as it is used to accomplish a multitude of tasks. One such example is in the field of multimedia quality assessment where subjective annotations can be used to train or evaluate quality prediction models. Lab-based tests could be one approach to get such quality annotations. They are usually performed in well-defined and controlled environments to ensure high reliability. However, this high reliability comes at a cost of higher time consumption and costs incurred. To mitigate this, crowd or online tests could be used. Usually, online tests cover a wider range of end devices, environmental conditions, or participants, which may have an impact on the ratings. To verify whether such online tests can be used for visual quality assessment, we designed three online tests. These online tests are based on previously conducted lab tests as this enables comparison of the results of both test paradigms. Our focus is on the quality assessment of high-resolution images and videos. The online tests use AVrate Voyager, which is a publicly accessible framework for online tests. To transform the lab tests into online tests, dedicated adaptations in the test methodologies are required. The considered modifications are, for example, a patch-based or centre cropping of the images and videos, or a randomly sub-sampling of the to-be-rated stimuli. Based on the analysis of the test results in terms of correlation and SOS analysis it is shown that online tests can be used as a reliable replacement for lab tests albeit with some limitations. These limitations relate to, e.g., lack of appropriate display devices, limitation of web technologies, and modern browsers considering support for different video codecs and formats.

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“…FIQA can be considered a specific subfield of image quality assessment (IQA), which has been a popular research topic in the literature recently [27,28]. A general overview about the field of IQA can be found in [29][30][31][32].…”

Section: Face Image Quality Assessmentmentioning

confidence: 99%

Benford’s Law and Perceptual Features for Face Image Quality Assessment

Varga

2023

Signals

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The rapid growth in multimedia, storage systems, and digital computers has resulted in huge repositories of multimedia content and large image datasets in recent years. For instance, biometric databases, which can be used to identify individuals based on fingerprints, facial features, or iris patterns, have gained a lot of attention both from academia and industry. Specifically, face image quality assessment (FIQA) has become a very important part of face recognition systems, since the performance of such systems strongly depends on the quality of input data, such as blur, focus, compression, pose, or illumination. The main contribution of this paper is an analysis of Benford’s law-inspired first digit distribution and perceptual features for FIQA. To be more specific, I investigate the first digit distributions in different domains, such as wavelet or singular values, as quality-aware features for FIQA. My analysis revealed that first digit distributions with perceptual features are able to reach a high performance in the task of FIQA.

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Disregarding the Big Picture: Towards Local Image Quality Assessment

Cited by 14 publications

References 17 publications

KonIQ-10k: Towards an ecologically valid and large-scale IQA database

KonIQ-10k: Towards an ecologically valid and large-scale IQA database

Quality assessment of higher resolution images and videos with remote testing

Benford’s Law and Perceptual Features for Face Image Quality Assessment

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