Reduced-Reference Quality Assessment Based on the Entropy of DWT
Coefficients of Locally Weighted Gradient Magnitudes
S. Alireza Golestaneh, and Lina J. Karam
This work presents a training-free
low-cost RRIQA method that requires a very small number of RR features
(6 RR features).
The proposed RRIQA algorithm is
based on the discrete wavelet transform (DWT) of locally weighted
gradient magnitudes.We apply human visual system’s contrast sensitivity
and neighborhood gradient
information to weight the gradient magnitudes in a locally adaptive
manner. The RR features are computed by measuring the entropy of each
DWT subband, for each scale, and pooling the subband entropies along all
orientations, resulting in L RR features (one average entropy per scale)
for an L-level DWT.
Extensive experiments performed on seven large-scale benchmark databases
demonstrate that the proposed RRIQA method delivers highly competitive
performance as compared to the state-of-the-art RRIQA models as well as
full reference ones for both natural and texture images.