  In this paper, we exploit the flexibility of multi-objective fitness
functions, and the efficiency of the model structure selection ability of a
standard genetic programming (GP) with the parameter estimation power of
classical regression via multi-gene genetic programming (MGGP), to propose a
new fusion technique for image quality assessment (IQA) that is called
Multi-measures Fusion based on Multi-Objective Genetic Programming (MFMOGP).
This technique can automatically select the most significant suitable measures,
from 16 full-reference IQA measures, used in aggregation and finds weights in a
weighted sum of their outputs while simultaneously optimizing for both accuracy
and complexity. The obtained well-performing fusion of IQA measures are
evaluated on four largest publicly available image databases and compared
against state-of-the-art full-reference IQA approaches. Results of comparison
reveal that the proposed approach outperforms other state-of-the-art recently
developed fusion approaches.
