Hybrid Image Resolution Quality Metric (HIRQM):A Comprehensive Perceptual Image Quality Assessment Framework

• URL: http://arxiv.org/abs/2505.02001v1
• Date: Sun, 04 May 2025 06:14:10 GMT
• Title: Hybrid Image Resolution Quality Metric (HIRQM):A Comprehensive Perceptual Image Quality Assessment Framework
• Authors: Vineesh Kumar Reddy Mondem,
• Abstract summary: We propose the Hybrid Image Resolution Quality Metric (HIRQM) to integrate statistical, multi-scale, and deep learning methods for a comprehensive quality evaluation.<n>A dynamic weighting mechanism adapts component contributions based on image characteristics like brightness and variance, enhancing flexibility across distortion types.<n> evaluated on TID2013 and LIVE datasets, HIRQM Pearson and Spearman correlations of 0.92 and 0.90, outperforming traditional metrics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional image quality assessment metrics like Mean Squared Error and Structural Similarity Index often fail to reflect perceptual quality under complex distortions. We propose the Hybrid Image Resolution Quality Metric (HIRQM), integrating statistical, multi-scale, and deep learning-based methods for a comprehensive quality evaluation. HIRQM combines three components: Probability Density Function for local pixel distribution analysis, Multi-scale Feature Similarity for structural integrity across resolutions, and Hierarchical Deep Image Features using a pre-trained VGG16 network for semantic alignment with human perception. A dynamic weighting mechanism adapts component contributions based on image characteristics like brightness and variance, enhancing flexibility across distortion types. Our contributions include a unified metric and dynamic weighting for better perceptual alignment. Evaluated on TID2013 and LIVE datasets, HIRQM achieves Pearson and Spearman correlations of 0.92 and 0.90, outperforming traditional metrics. It excels in handling noise, blur, and compression artifacts, making it valuable for image processing applications like compression and restoration.

Related papers

• Subjective Visual Quality Assessment for High-Fidelity Learning-Based Image Compression [2.296138318128071]
  We present a comprehensive subjective visual quality assessment of JPEG AI-compressed images using the JPEG AIC-3 methodology.<n>We reconstructed JND-based quality scales using a unified model based on boosted and plain triplet comparisons.<n>The CVVDP metric achieved the overall highest performance; however, most metrics including CVDP were overly optimistic in predicting the quality of JPEG AI-compressed images.
  arXiv  Detail & Related papers  (2025-04-07T15:16:58Z)
• Predicting Satisfied User and Machine Ratio for Compressed Images: A Unified Approach [58.71009078356928]
  We create a deep learning-based model to predict Satisfied User Ratio (SUR) and Satisfied Machine Ratio (SMR) of compressed images simultaneously.<n> Experimental results indicate that the proposed model significantly outperforms state-of-the-art SUR and SMR prediction methods.
  arXiv  Detail & Related papers  (2024-12-23T11:09:30Z)
• PIGUIQA: A Physical Imaging Guided Perceptual Framework for Underwater Image Quality Assessment [59.9103803198087]
  We propose a Physical Imaging Guided perceptual framework for Underwater Image Quality Assessment (UIQA)<n>By leveraging underwater radiative transfer theory, we integrate physics-based imaging estimations to establish quantitative metrics for these distortions.<n>The proposed model accurately predicts image quality scores and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-12-20T03:31:45Z)
• Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization Problem [23.833099288826045]
  Training Single-Image Super-Resolution (SISR) models using pixel-based regression losses can achieve high distortion metrics scores. However, they often results in blurry images due to insufficient recovery of high-frequency details. We propose a novel method that incorporates Multi-Objective Optimization (MOO) into the training process of SISR models to balance perceptual quality and distortion.
  arXiv  Detail & Related papers  (2024-09-05T02:14:04Z)
• Semantic Ensemble Loss and Latent Refinement for High-Fidelity Neural Image Compression [58.618625678054826]
  This study presents an enhanced neural compression method designed for optimal visual fidelity. We have trained our model with a sophisticated semantic ensemble loss, integrating Charbonnier loss, perceptual loss, style loss, and a non-binary adversarial loss. Our empirical findings demonstrate that this approach significantly improves the statistical fidelity of neural image compression.
  arXiv  Detail & Related papers  (2024-01-25T08:11:27Z)
• Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity [55.399230250413986]
  We propose a Quality-Aware Feature Matching IQA Metric (QFM-IQM) to remove harmful semantic noise features from the upstream task. Our approach achieves superior performance to the state-of-the-art NR-IQA methods on eight standard IQA datasets.
  arXiv  Detail & Related papers  (2023-12-11T06:50:27Z)
• 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)
• DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator [53.57431705309919]
  ImageNet pre-trained deep neural networks (DNNs) show notable transferability for building effective image quality assessment (IQA) models. We develop a novel full-reference IQA (FR-IQA) model based exclusively on pre-trained DNN features. We conduct comprehensive experiments to demonstrate the superiority of the proposed quality model on five standard IQA datasets.
  arXiv  Detail & Related papers  (2022-11-09T14:57:27Z)
• Rank-Enhanced Low-Dimensional Convolution Set for Hyperspectral Image Denoising [50.039949798156826]
  This paper tackles the challenging problem of hyperspectral (HS) image denoising. We propose rank-enhanced low-dimensional convolution set (Re-ConvSet) We then incorporate Re-ConvSet into the widely-used U-Net architecture to construct an HS image denoising method.
  arXiv  Detail & Related papers  (2022-07-09T13:35:12Z)
• SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution [24.584839578742237]
  Super-Resolution technique has greatly improved the visual quality of images by enhancing their resolutions. It also calls for an efficient SR Image Quality Assessment (SR-IQA) to evaluate those algorithms or their generated images. In emerging deep-learning-based SR, a generated high-quality, visually pleasing image may have different structures from its corresponding low-quality image.
  arXiv  Detail & Related papers  (2022-05-07T07:52:55Z)
• A study of deep perceptual metrics for image quality assessment [3.254879465902239]
  We study perceptual metrics based on deep neural networks for tackling the Image Quality Assessment (IQA) task. We propose our multi-resolution perceptual metric (MR-Perceptual) that allows us to aggregate perceptual information at different resolutions.
  arXiv  Detail & Related papers  (2022-02-17T14:52:53Z)
• 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)

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.