AIGCOIQA2024: Perceptual Quality Assessment of AI Generated Omnidirectional Images

• URL: http://arxiv.org/abs/2404.01024v1
• Date: Mon, 1 Apr 2024 10:08:23 GMT
• Title: AIGCOIQA2024: Perceptual Quality Assessment of AI Generated Omnidirectional Images
• Authors: Liu Yang, Huiyu Duan, Long Teng, Yucheng Zhu, Xiaohong Liu, Menghan Hu, Xiongkuo Min, Guangtao Zhai, Patrick Le Callet,
• Abstract summary: We establish a large-scale AI generated omnidirectional image IQA database named AIGCOIQA2024. A subjective IQA experiment is conducted to assess human visual preferences from three perspectives. We conduct a benchmark experiment to evaluate the performance of state-of-the-art IQA models on our database.
• Score: 70.42666704072964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, the rapid advancement of Artificial Intelligence Generated Content (AIGC) has attracted widespread attention. Among the AIGC, AI generated omnidirectional images hold significant potential for Virtual Reality (VR) and Augmented Reality (AR) applications, hence omnidirectional AIGC techniques have also been widely studied. AI-generated omnidirectional images exhibit unique distortions compared to natural omnidirectional images, however, there is no dedicated Image Quality Assessment (IQA) criteria for assessing them. This study addresses this gap by establishing a large-scale AI generated omnidirectional image IQA database named AIGCOIQA2024 and constructing a comprehensive benchmark. We first generate 300 omnidirectional images based on 5 AIGC models utilizing 25 text prompts. A subjective IQA experiment is conducted subsequently to assess human visual preferences from three perspectives including quality, comfortability, and correspondence. Finally, we conduct a benchmark experiment to evaluate the performance of state-of-the-art IQA models on our database. The database will be released to facilitate future research.

Related papers

• Understanding and Evaluating Human Preferences for AI Generated Images with Instruction Tuning [58.41087653543607]
  We first establish a novel Image Quality Assessment (IQA) database for AIGIs, termed AIGCIQA2023+. This paper presents a MINT-IQA model to evaluate and explain human preferences for AIGIs from Multi-perspectives with INstruction Tuning.
  arXiv  Detail & Related papers  (2024-05-12T17:45:11Z)
• PKU-AIGIQA-4K: A Perceptual Quality Assessment Database for Both Text-to-Image and Image-to-Image AI-Generated Images [1.5265677582796984]
  We establish a large scale perceptual quality assessment database for both text-to-image and image-to-image AIGIs, named PKU-AIGIQA-4K. We propose three image quality assessment (IQA) methods based on pre-trained models that include a no-reference method NR-AIGCIQA, a full-reference method FR-AIGCIQA, and a partial-reference method PR-AIGCIQA.
  arXiv  Detail & Related papers  (2024-04-29T03:57:43Z)
• AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment [54.93996119324928]
  We create the largest AIGI subjective quality database to date with 20,000 AIGIs and 420,000 subjective ratings, known as AIGIQA-20K. We conduct benchmark experiments on this database to assess the correspondence between 16 mainstream AIGI quality models and human perception.
  arXiv  Detail & Related papers  (2024-04-04T12:12:24Z)
• PKU-I2IQA: An Image-to-Image Quality Assessment Database for AI Generated Images [1.6031185986328562]
  We establish a human perception-based image-to-image AIGCIQA database, named PKU-I2IQA. We propose two benchmark models: NR-AIGCIQA based on the no-reference image quality assessment method and FR-AIGCIQA based on the full-reference image quality assessment method.
  arXiv  Detail & Related papers  (2023-11-27T05:53:03Z)
• AIGCIQA2023: A Large-scale Image Quality Assessment Database for AI Generated Images: from the Perspectives of Quality, Authenticity and Correspondence [42.85549933048976]
  We first generate over 2000 images based on 6 state-of-the-art text-to-image generation models using 100 prompts. Based on these images, a subjective experiment is conducted to assess the human visual preferences for each image from three perspectives. We conduct a benchmark experiment to evaluate the performance of several state-of-the-art IQA metrics on our constructed database.
  arXiv  Detail & Related papers  (2023-07-01T03:30:31Z)
• AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment [62.8834581626703]
  We build the most comprehensive subjective quality database AGIQA-3K so far. We conduct a benchmark experiment on this database to evaluate the consistency between the current Image Quality Assessment (IQA) model and human perception. We believe that the fine-grained subjective scores in AGIQA-3K will inspire subsequent AGI quality models to fit human subjective perception mechanisms.
  arXiv  Detail & Related papers  (2023-06-07T18:28:21Z)
• Perceptual Quality Assessment of Omnidirectional Images [81.76416696753947]
  We first establish an omnidirectional IQA (OIQA) database, which includes 16 source images and 320 distorted images degraded by 4 commonly encountered distortion types. Then a subjective quality evaluation study is conducted on the OIQA database in the VR environment. The original and distorted omnidirectional images, subjective quality ratings, and the head and eye movement data together constitute the OIQA database.
  arXiv  Detail & Related papers  (2022-07-06T13:40:38Z)
• Confusing Image Quality Assessment: Towards Better Augmented Reality Experience [96.29124666702566]
  We consider AR technology as the superimposition of virtual scenes and real scenes, and introduce visual confusion as its basic theory. A ConFusing Image Quality Assessment (CFIQA) database is established, which includes 600 reference images and 300 distorted images generated by mixing reference images in pairs. An objective metric termed CFIQA is also proposed to better evaluate the confusing image quality.
  arXiv  Detail & Related papers  (2022-04-11T07:03:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.