Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning Article Swipe
Kwan-Yee Lin
,
Guanxiang Wang
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1804.01681
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1804.01681
No-reference image quality assessment (NR-IQA) is a fundamental yet challenging task in low-level computer vision community. The difficulty is particularly pronounced for the limited information, for which the corresponding reference for comparison is typically absent. Although various feature extraction mechanisms have been leveraged from natural scene statistics to deep neural networks in previous methods, the performance bottleneck still exists. In this work, we propose a hallucination-guided quality regression network to address the issue. We firstly generate a hallucinated reference constrained on the distorted image, to compensate the absence of the true reference. Then, we pair the information of hallucinated reference with the distorted image, and forward them to the regressor to learn the perceptual discrepancy with the guidance of an implicit ranking relationship within the generator, and therefore produce the precise quality prediction. To demonstrate the effectiveness of our approach, comprehensive experiments are evaluated on four popular image quality assessment benchmarks. Our method significantly outperforms all the previous state-of-the-art methods by large margins. The code and model will be publicly available on the project page https://kwanyeelin.github.io/projects/HIQA/HIQA.html.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1804.01681
- https://arxiv.org/pdf/1804.01681
- OA Status
- green
- Cited By
- 34
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2795855127
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2795855127Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1804.01681Digital Object Identifier
- Title
-
Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial LearningWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
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2018-04-05Full publication date if available
- Authors
-
Kwan-Yee Lin, Guanxiang WangList of authors in order
- Landing page
-
https://arxiv.org/abs/1804.01681Publisher landing page
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https://arxiv.org/pdf/1804.01681Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1804.01681Direct OA link when available
- Concepts
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Hallucinating, Computer science, Artificial intelligence, Image quality, Quality (philosophy), Code (set theory), Machine learning, Ranking (information retrieval), Image (mathematics), Data mining, Set (abstract data type), Programming language, Epistemology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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34Total citation count in OpenAlex
- Citations by year (recent)
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2024: 3, 2023: 7, 2022: 3, 2021: 5, 2020: 10Per-year citation counts (last 5 years)
- References (count)
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35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.margins. | 162 |
| abstract_inverted_index.methods, | 53 |
| abstract_inverted_index.networks | 50 |
| abstract_inverted_index.previous | 52, 157 |
| abstract_inverted_index.publicly | 169 |
| abstract_inverted_index.approach, | 139 |
| abstract_inverted_index.available | 170 |
| abstract_inverted_index.distorted | 82, 102 |
| abstract_inverted_index.evaluated | 143 |
| abstract_inverted_index.leveraged | 42 |
| abstract_inverted_index.low-level | 12 |
| abstract_inverted_index.reference | 29, 78, 99 |
| abstract_inverted_index.regressor | 109 |
| abstract_inverted_index.therefore | 127 |
| abstract_inverted_index.typically | 33 |
| abstract_inverted_index.assessment | 3, 149 |
| abstract_inverted_index.bottleneck | 56 |
| abstract_inverted_index.community. | 15 |
| abstract_inverted_index.comparison | 31 |
| abstract_inverted_index.compensate | 85 |
| abstract_inverted_index.difficulty | 17 |
| abstract_inverted_index.extraction | 38 |
| abstract_inverted_index.generator, | 125 |
| abstract_inverted_index.mechanisms | 39 |
| abstract_inverted_index.perceptual | 113 |
| abstract_inverted_index.pronounced | 20 |
| abstract_inverted_index.reference. | 91 |
| abstract_inverted_index.regression | 67 |
| abstract_inverted_index.statistics | 46 |
| abstract_inverted_index.benchmarks. | 150 |
| abstract_inverted_index.challenging | 9 |
| abstract_inverted_index.constrained | 79 |
| abstract_inverted_index.demonstrate | 134 |
| abstract_inverted_index.discrepancy | 114 |
| abstract_inverted_index.experiments | 141 |
| abstract_inverted_index.fundamental | 7 |
| abstract_inverted_index.information | 96 |
| abstract_inverted_index.outperforms | 154 |
| abstract_inverted_index.performance | 55 |
| abstract_inverted_index.prediction. | 132 |
| abstract_inverted_index.No-reference | 0 |
| abstract_inverted_index.hallucinated | 77, 98 |
| abstract_inverted_index.information, | 24 |
| abstract_inverted_index.particularly | 19 |
| abstract_inverted_index.relationship | 122 |
| abstract_inverted_index.comprehensive | 140 |
| abstract_inverted_index.corresponding | 28 |
| abstract_inverted_index.effectiveness | 136 |
| abstract_inverted_index.significantly | 153 |
| abstract_inverted_index.state-of-the-art | 158 |
| abstract_inverted_index.hallucination-guided | 65 |
| abstract_inverted_index.https://kwanyeelin.github.io/projects/HIQA/HIQA.html. | 175 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile |