A low complexity wavelet-based blind image quality evaluator

Heydari, Maryam; Cheraaqee, Pooryaa; Mansouri, Azadeh; Mahmoudi-Aznaveh, Ahmad

doi:10.1016/j.image.2018.12.016

Cited by 9 publications

(1 citation statement)

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“…In most of the applications, the quality of an image is momentous and a number of techniques are currently available for the evaluation of image quality [1][2][3][4][5]. There are mainly two classes of such evaluation techniques, the first class requires reference images [6][7][8] while the second class does not require any reference image [9].…”

Section: Introductionmentioning

confidence: 99%

PIQI: perceptual image quality index based on ensemble of Gaussian process regression

Ahmed

Asif

Khalid

2021

Multimed Tools Appl

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Digital images contain a lot of redundancies, therefore, compression techniques are applied to reduce the image size without the 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's 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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