Comprehensive evaluation of no-reference image quality assessment algorithms on KADID-10k database

• URL: http://arxiv.org/abs/2010.09414v2
• Date: Mon, 9 Nov 2020 10:07:54 GMT
• Title: Comprehensive evaluation of no-reference image quality assessment algorithms on KADID-10k database
• Authors: Domonkos Varga
• Abstract summary: The evaluation of objective image quality assessment algorithms is based on experiments conducted on publicly available benchmark databases. Average PLCC, SROCC, and KROCC are reported which were measured over 100 random train-test splits. Our results may be helpful to obtain a clear understanding about the status of state-of-the-art no-reference image quality assessment methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The main goal of objective image quality assessment is to devise computational, mathematical models which are able to predict perceptual image quality consistently with subjective evaluations. The evaluation of objective image quality assessment algorithms is based on experiments conducted on publicly available benchmark databases. In this study, our goal is to give a comprehensive evaluation about no-reference image quality assessment algorithms, whose original source codes are available online, using the recently published KADID-10k database which is one of the largest available benchmark databases. Specifically, average PLCC, SROCC, and KROCC are reported which were measured over 100 random train-test splits. Furthermore, the database was divided into a train (appx. 80\% of images) and a test set (appx. 20% of images) with respect to the reference images. So no semantic content overlap was between these two sets. Our evaluation results may be helpful to obtain a clear understanding about the status of state-of-the-art no-reference image quality assessment methods.

Related papers

• Rank-based No-reference Quality Assessment for Face Swapping [88.53827937914038]
  The metric of measuring the quality in most face swapping methods relies on several distances between the manipulated images and the source image. We present a novel no-reference image quality assessment (NR-IQA) method specifically designed for face swapping.
  arXiv  Detail & Related papers  (2024-06-04T01:36:29Z)
• Holistic Evaluation of Text-To-Image Models [153.47415461488097]
  We introduce a new benchmark, Holistic Evaluation of Text-to-Image Models (HEIM) We identify 12 aspects, including text-image alignment, image quality, aesthetics, originality, reasoning, knowledge, bias, toxicity, fairness, robustness, multilinguality, and efficiency. Our results reveal that no single model excels in all aspects, with different models demonstrating different strengths.
  arXiv  Detail & Related papers  (2023-11-07T19:00:56Z)
• 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)
• PIQI: Perceptual Image Quality Index based on Ensemble of Gaussian Process Regression [2.9412539021452715]
  Perceptual Image Quality Index (PIQI) is proposed to assess the quality of digital images. The performance of the PIQI is checked on six benchmark databases and compared with twelve state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-16T06:44:17Z)
• 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)
• BIQ2021: A Large-Scale Blind Image Quality Assessment Database [1.3670071336891754]
  The Blind Image Quality Assessment Database, BIQ2021, is presented in this article. The dataset consists of three sets of images: those taken without the intention of using them for image quality assessment, those taken with intentionally introduced natural distortions, and those taken from an open-source image-sharing platform. The database contains information about subjective scoring, human subject statistics, and the standard deviation of each image.
  arXiv  Detail & Related papers  (2022-02-08T14:07:38Z)
• A survey on IQA [0.0]
  This article will review the concepts and metrics of image quality assessment and also video quality assessment. It briefly introduce some methods of full-reference and semi-reference image quality assessment, and focus on the non-reference image quality assessment methods based on deep learning.
  arXiv  Detail & Related papers  (2021-08-29T10:52:27Z)
• Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment [157.1292674649519]
  We propose a practical solution named degraded-reference IQA (DR-IQA) DR-IQA exploits the inputs of IR models, degraded images, as references. Our results can even be close to the performance of full-reference settings.
  arXiv  Detail & Related papers  (2021-08-18T02:35:08Z)
• Comprehensive evaluation of no-reference image quality assessment algorithms on authentic distortions [0.0]
  No-reference image quality assessment predicts the quality of a given input image without any knowledge or information about its pristine (distortion free) counterpart. In this study, we evaluate several machine learning based NR-IQA methods and one opinion unaware method on databases consisting of authentic distortions.
  arXiv  Detail & Related papers  (2020-10-26T21:25:46Z)
• 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.