A novel pooling strategy for Full Reference Image Quality Assessment based on harmonic means

The most perceptual Full Reference Image Quality Assessment metrics (FR-IQA) shared a common two-step model; local quality measurement, and pooling. In this letter, a novel pooling strategy based on harmonic mean is proposed to predict the final quality score in FR-IQA. In contrast to arithmetic mean, the harmonic mean tends to emphasize the contributions from the local severely distorted regions or pixels in the definition of assessment function using reciprocal transformation. It is derived from the observations that humans visual attention is mostly affected with the region having severely distorted points or regions. In addition, the relationship of subjective visual quality with the quality score against different levels of distortion in the images is described as a non-linear procedure by introducing another reciprocal transformation in harmonic mean. The proposed pooling strategy is applied to some popular FR-IQA metrics, including SSIM, GSSIM, and FSIM. The experimental results have demonstrated that the metrics with proposed pooling strategy have better performances compared to the standard versions, especially on the images with small but seriously distorted regions. The proposed pooling strategy is computationally very efficient since only one averaging operation and two reciprocal transformations are required.