No-reference Quality Assessment of Contrast-distorted Images using Contrast-enhanced Pseudo Reference

• URL: http://arxiv.org/abs/2510.05053v1
• Date: Mon, 06 Oct 2025 17:32:48 GMT
• Title: No-reference Quality Assessment of Contrast-distorted Images using Contrast-enhanced Pseudo Reference
• Authors: Mohammad-Ali Mahmoudpour, Saeed Mahmoudpour,
• Abstract summary: We propose a no-reference image quality assessment (NR-IQA) metric for contrast-distorted images.<n>Using a set of contrast enhancement algorithms, we aim to generate pseudo-reference images that are visually close to the actual reference image.<n>A large dataset of contrast-enhanced images is produced to train a classification network that can select the most suitable contrast enhancement algorithm.
• Score: 1.7188280334580188
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contrast change is an important factor that affects the quality of images. During image capturing, unfavorable lighting conditions can cause contrast change and visual quality loss. While various methods have been proposed to assess the quality of images under different distortions such as blur and noise, contrast distortion has been largely overlooked as its visual impact and properties are different from other conventional types of distortions. In this paper, we propose a no-reference image quality assessment (NR-IQA) metric for contrast-distorted images. Using a set of contrast enhancement algorithms, we aim to generate pseudo-reference images that are visually close to the actual reference image, such that the NR problem is transformed to a Full-reference (FR) assessment with higher accuracy. To this end, a large dataset of contrast-enhanced images is produced to train a classification network that can select the most suitable contrast enhancement algorithm based on image content and distortion for pseudo-reference image generation. Finally, the evaluation is performed in the FR manner to assess the quality difference between the contrast-enhanced (pseudoreference) and degraded images. Performance evaluation of the proposed method on three databases containing contrast distortions (CCID2014, TID2013, and CSIQ), indicates the promising performance of the proposed method.

Related papers

• No-Reference Image Contrast Assessment with Customized EfficientNet-B0 [3.4527546378946]
  No reference image quality assessment NR IQA models struggled to accurately evaluate contrast distortions under diverse real world conditions.<n>In this study, we proposed a deep learning based framework for blind contrast quality assessment.<n>Models are modified with a contrast-aware regression head and trained end to end using targeted data augmentations.
  arXiv  Detail & Related papers  (2025-09-26T06:54:37Z)
• Gadolinium dose reduction for brain MRI using conditional deep learning [66.99830668082234]
  Two main challenges for these approaches are the accurate prediction of contrast enhancement and the synthesis of realistic images. We address both challenges by utilizing the contrast signal encoded in the subtraction images of pre-contrast and post-contrast image pairs. We demonstrate the effectiveness of our approach on synthetic and real datasets using various scanners, field strengths, and contrast agents.
  arXiv  Detail & Related papers  (2024-03-06T08:35:29Z)
• 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)
• Image Deblurring by Exploring In-depth Properties of Transformer [86.7039249037193]
  We leverage deep features extracted from a pretrained vision transformer (ViT) to encourage recovered images to be sharp without sacrificing the performance measured by the quantitative metrics. By comparing the transformer features between recovered image and target one, the pretrained transformer provides high-resolution blur-sensitive semantic information. One regards the features as vectors and computes the discrepancy between representations extracted from recovered image and target one in Euclidean space.
  arXiv  Detail & Related papers  (2023-03-24T14:14:25Z)
• Dehazed Image Quality Evaluation: From Partial Discrepancy to Blind Perception [35.257798506356814]
  Image dehazing aims to restore spatial details from hazy images. We propose a Reduced-Reference dehazed image quality evaluation approach based on Partial Discrepancy. We extend it to a No-Reference quality assessment metric with Blind Perception.
  arXiv  Detail & Related papers  (2022-11-22T23:49:14Z)
• Image Quality Assessment: Learning to Rank Image Distortion Level [0.0]
  We learn to compare the image quality of two registered images, with respect to a chosen distortion. Our method takes advantage of the fact that at times, simulating image distortion and later evaluating its relative image quality, is easier than assessing its absolute value.
  arXiv  Detail & Related papers  (2022-08-04T18:33:33Z)
• Treatment Learning Causal Transformer for Noisy Image Classification [62.639851972495094]
  In this work, we incorporate this binary information of "existence of noise" as treatment into image classification tasks to improve prediction accuracy. Motivated from causal variational inference, we propose a transformer-based architecture, that uses a latent generative model to estimate robust feature representations for noise image classification. We also create new noisy image datasets incorporating a wide range of noise factors for performance benchmarking.
  arXiv  Detail & Related papers  (2022-03-29T13:07:53Z)
• Modulated Contrast for Versatile Image Synthesis [60.304183493234376]
  MoNCE is a versatile metric that introduces image contrast to learn a calibrated metric for the perception of multifaceted inter-image distances. We introduce optimal transport in MoNCE to modulate the pushing force of negative samples collaboratively across multiple contrastive objectives.
  arXiv  Detail & Related papers  (2022-03-17T14:03:46Z)
• Non-Reference Quality Monitoring of Digital Images using Gradient Statistics and Feedforward Neural Networks [0.1657441317977376]
  A non-reference quality metric is proposed to assess the quality of digital images. The proposed metric is computationally faster than its counterparts and can be used for the quality assessment of image sequences.
  arXiv  Detail & Related papers  (2021-12-27T20:21:55Z)
• 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)
• 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)

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.