Sampled efficient full-reference image quality assessment models

TLDR: A highly efficient grid sampling scheme is proposed which replaces the ubiquitous convolution operations with local block-based multiplications and can yield results similar to methods that use a complete image feature map, even when the number of feature samples is reduced by 90%.

Abstract: Existing Ml-reference image quality assessment models first compute a full image quality-predictive feature map followed by a spatial pooling scheme, thereby producing a single quality score. Here we study spatial sampling strategies that can be used to more efficiently compute reliable picture quality scores. We develop a random sampling scheme on single scale full-reference image quality assessment models. Based on a thorough analysis of how this random sampling strategy affects the correlations of the resulting pooled scores against human subjective quality judgements, a highly efficient grid sampling scheme is proposed which replaces the ubiquitous convolution operations with local block-based multiplications. Experiments on four different databases show that this block-based sampling strategy can yield results similar to methods that use a complete image feature map, even when the number of feature samples is reduced by 90%.