PIQI: Perceptual Image Quality Index based on Ensemble of Gaussian Process Regression

• URL: http://arxiv.org/abs/2305.09214v1
• Date: Tue, 16 May 2023 06:44:17 GMT
• Title: PIQI: Perceptual Image Quality Index based on Ensemble of Gaussian Process Regression
• Authors: Nisar Ahmed, Hafiz Muhammad Shahzad Asif, and Hassan Khalid
• Abstract summary: 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.
• Score: 2.9412539021452715
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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