PIQI: Perceptual Image Quality Index based on Ensemble of Gaussian
  Process Regression
Nisar Ahmed Hafiz Muhammad Shahzad Asif Hassan Khalid
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.