Comprehensive evaluation of no-reference image quality assessment algorithms on authentic distortions

• URL: http://arxiv.org/abs/2011.07950v1
• Date: Mon, 26 Oct 2020 21:25:46 GMT
• Title: Comprehensive evaluation of no-reference image quality assessment algorithms on authentic distortions
• Authors: Domonkos Varga
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Objective image quality assessment deals with the prediction of digital images' perceptual quality. No-reference image quality assessment predicts the quality of a given input image without any knowledge or information about its pristine (distortion free) counterpart. Machine learning algorithms are heavily used in no-reference image quality assessment because it is very complicated to model the human visual system's quality perception. Moreover, no-reference image quality assessment algorithms are evaluated on publicly available benchmark databases. These databases contain images with their corresponding quality scores. In this study, we evaluate several machine learning based NR-IQA methods and one opinion unaware method on databases consisting of authentic distortions. Specifically, LIVE In the Wild and KonIQ-10k databases were applied to evaluate the state-of-the-art. For machine learning based methods, appx. 80% were used for training and the remaining 20% were used for testing. Furthermore, average PLCC, SROCC, and KROCC values were reported over 100 random train-test splits. The statistics of PLCC, SROCC, and KROCC values were also published using boxplots. 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

• Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment [58.09173822651016]
  We argue that it is possible to learn reference knowledge under the No-Reference Image Quality Assessment setting. We propose a new framework to learn comparative knowledge from non-aligned reference images. Experiments on eight standard NR-IQA datasets demonstrate the superior performance to the state-of-the-art NR-IQA methods.
  arXiv  Detail & Related papers  (2023-12-01T13:56:01Z)
• Helping Visually Impaired People Take Better Quality Pictures [52.03016269364854]
  We develop tools to help visually impaired users minimize occurrences of common technical distortions. We also create a prototype feedback system that helps to guide users to mitigate quality issues.
  arXiv  Detail & Related papers  (2023-05-14T04:37:53Z)
• Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images [57.02760260360728]
  We build a large-scale in-the-wild CGIQA database consisting of 6,000 CGIs (CGIQA-6k) We propose an effective deep learning-based no-reference (NR) IQA model by utilizing both distortion and aesthetic quality representation. Experimental results show that the proposed method outperforms all other state-of-the-art NR IQA methods on the constructed CGIQA-6k database.
  arXiv  Detail & Related papers  (2023-03-14T16:32:24Z)
• 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)
• 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)
• CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability [2.3624125155742055]
  We propose a novel learning paradigm that learns internal network observations during the training process. Our proposed CR-FIQA uses this paradigm to estimate the face image quality of a sample by predicting its relative classifiability. We demonstrate the superiority of our proposed CR-FIQA over state-of-the-art (SOTA) FIQA algorithms.
  arXiv  Detail & Related papers  (2021-12-13T12:18:43Z)
• Image Quality Assessment using Contrastive Learning [50.265638572116984]
  We train a deep Convolutional Neural Network (CNN) using a contrastive pairwise objective to solve the auxiliary problem. We show through extensive experiments that CONTRIQUE achieves competitive performance when compared to state-of-the-art NR image quality models. Our results suggest that powerful quality representations with perceptual relevance can be obtained without requiring large labeled subjective image quality datasets.
  arXiv  Detail & Related papers  (2021-10-25T21:01:00Z)
• 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 KADID-10k database [0.0]
  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.
  arXiv  Detail & Related papers  (2020-10-19T12:23:06Z)

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.