PIQI: Perceptual Image Quality Index based on Ensemble of Gaussian Process Regression
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Abstract (arXiv)
Digital images contain a lot of redundancies, therefore, compression techniques are applied to reduce the image size without loss of reasonable image quality. Same become more prominent in the case of videos which contains image sequences and higher compression ratios are achieved in low throughput networks. Assessment of quality of images in such scenarios has become of particular interest. Subjective evaluation in most of the scenarios is infeasible so objective evaluation is preferred. Among the three objective quality measures, full-reference and reduced-reference methods require an original image in some form to calculate the image quality which is unfeasible in scenarios such as broadcasting, acquisition or enhancement. Therefore, a no-reference Perceptual Image Quality Index (PIQI) is proposed in this paper to assess the quality of digital images which calculates luminance and gradient statistics along with mean subtracted contrast normalized products in multiple scales and color spaces. These extracted features are provided to a stacked ensemble of Gaussian Process Regression (GPR) to perform the perceptual quality evaluation. The performance of the PIQI is checked on six benchmark databases and compared with twelve state-of-the-art methods and competitive results are achieved. The comparison is made based on RMSE, Pearson and Spearman correlation coefficients between ground truth and predicted quality scores. The scores of 0.0552, 0.9802 and 0.9776 are achieved respectively for these metrics on CSIQ database. Two cross-dataset evaluation experiments are performed to check the generalization of PIQI.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 5 of its 54 references that are in the map. First posted on arXiv 16 May 2023.
Builds on
5 of 54 references.
References · 5
- 2016Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessmentcited by 224
- 2019dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairscited by 73
- 2016A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessmentcited by 57
- 2016On the Use of Deep Learning for Blind Image Quality Assessmentcited by 34
- 2018A Convolutional Neural Networks Denoising Approach for Salt and Pepper Noisecited by 4
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Citing papers per year: 2023: 1; 2024: 0; 2025: 5.
6 papers.
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- 2023Blind Image Quality Assessment Using Multi-Stream Architecture with Spatial and Channel Attentioncited by 0
- 2025MetaQAP - A Meta-Learning Approach for Quality-Aware Pretraining in Image Quality Assessmentcited by 0
- 2025Enhancing Wireless Device Identification through RF Fingerprinting: Leveraging Transient Energy Spectrum Analysiscited by 0
- 2025A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancementcited by 0
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- 2025Deep Learning-Based Age Estimation and Gender Deep Learning-Based Age Estimation and Gender Classification for Targeted Advertisement24 Jul 2025
- 2025A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancement23 Jun 2025
- 2025Enhancing Wireless Device Identification through RF Fingerprinting: Leveraging Transient Energy Spectrum Analysis20 Jun 2025
- 2025MetaQAP - A Meta-Learning Approach for Quality-Aware Pretraining in Image Quality Assessment19 Jun 2025
- 2025A Robust Deep Networks based Multi-Object MultiCamera Tracking System for City Scale Traffic1 May 2025
- 2023Blind Image Quality Assessment Using Multi-Stream Architecture with Spatial and Channel Attention19 Jul 2023