Exploring Image Quality Assessment from a New Perspective: Pupil Size

• URL: http://arxiv.org/abs/2505.13841v1
• Date: Tue, 20 May 2025 02:27:34 GMT
• Title: Exploring Image Quality Assessment from a New Perspective: Pupil Size
• Authors: Yixuan Gao, Xiongkuo Min, Guangtao Zhai,
• Abstract summary: This paper explores how the image quality assessment (IQA) task affects the cognitive processes of people from the perspective of pupil size.<n>By analyzing the difference in pupil size between the two tasks, we find that people may activate the visual attention mechanism when evaluating image quality.
• Score: 58.577929564744146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores how the image quality assessment (IQA) task affects the cognitive processes of people from the perspective of pupil size and studies the relationship between pupil size and image quality. Specifically, we first invited subjects to participate in a subjective experiment, which includes two tasks: free observation and IQA. In the free observation task, subjects did not need to perform any action, and they only needed to observe images as they usually do with an album. In the IQA task, subjects were required to score images according to their overall impression of image quality. Then, by analyzing the difference in pupil size between the two tasks, we find that people may activate the visual attention mechanism when evaluating image quality. Meanwhile, we also find that the change in pupil size is closely related to image quality in the IQA task. For future research on IQA, this research can not only provide a theoretical basis for the objective IQA method and promote the development of more effective objective IQA methods, but also provide a new subjective IQA method for collecting the authentic subjective impression of image quality.

Related papers

• VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank [23.613534906344753]
  We introduce VisualQuality-R1, a reasoning-induced no-reference IQA (NR-IQA) model.<n>We train it with reinforcement learning to rank, a learning algorithm tailored to the intrinsically relative nature of visual quality.<n>In experiments, VisualQuality-R1 consistently outperforms discriminative deep learning-based NR-IQA models.
  arXiv  Detail & Related papers  (2025-05-20T14:56:50Z)
• Embodied Image Quality Assessment for Robotic Intelligence [36.80460733311791]
  We first propose an embodied image quality assessment (EIQA) frameworks.<n>We establish assessment metrics for input images based on the downstream tasks of robot.<n>Experiments demonstrate that quality assessment of embodied images is different from that of humans.
  arXiv  Detail & Related papers  (2024-12-25T04:29:22Z)
• UniQA: Unified Vision-Language Pre-training for Image Quality and Aesthetic Assessment [47.207432606050105]
  Image Quality Assessment (IQA) and Image Aesthetic Assessment (IAA) aim to simulate human subjective perception of image visual quality and aesthetic appeal.<n>We propose Unified vision-language pre-training of Quality and Aesthetics (UniQA) to extract useful and common representations from two tasks.
  arXiv  Detail & Related papers  (2024-06-03T07:40:10Z)
• Let's ViCE! Mimicking Human Cognitive Behavior in Image Generation Evaluation [96.74302670358145]
  We introduce an automated method for Visual Concept Evaluation (ViCE) to assess consistency between a generated/edited image and the corresponding prompt/instructions. ViCE combines the strengths of Large Language Models (LLMs) and Visual Question Answering (VQA) into a unified pipeline, aiming to replicate the human cognitive process in quality assessment.
  arXiv  Detail & Related papers  (2023-07-18T16:33:30Z)
• Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective [93.56647950778357]
  Blind image quality assessment (BIQA) predicts the human perception of image quality without any reference information. We develop a general and automated multitask learning scheme for BIQA to exploit auxiliary knowledge from other tasks.
  arXiv  Detail & Related papers  (2023-03-27T07:58:09Z)
• Conformer and Blind Noisy Students for Improved Image Quality Assessment [80.57006406834466]
  Learning-based approaches for perceptual image quality assessment (IQA) usually require both the distorted and reference image for measuring the perceptual quality accurately. In this work, we explore the performance of transformer-based full-reference IQA models. We also propose a method for IQA based on semi-supervised knowledge distillation from full-reference teacher models into blind student models.
  arXiv  Detail & Related papers  (2022-04-27T10:21:08Z)
• 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)
• Cuid: A new study of perceived image quality and its subjective assessment [30.698984450985318]
  We present a new study of image quality perception where subjective ratings were collected in a controlled lab environment. We investigate how quality perception is affected by a combination of different categories of images and different types and levels of distortions.
  arXiv  Detail & Related papers  (2020-09-28T13:14:45Z)
• Object-QA: Towards High Reliable Object Quality Assessment [71.71188284059203]
  In object recognition applications, object images usually appear with different quality levels. We propose an effective approach named Object-QA to assess high-reliable quality scores for object images.
  arXiv  Detail & Related papers  (2020-05-27T01:46:58Z)

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.