Multi-scale SSIM metric based on weighted wavelet decomposition

Abstract Image quality assessment aims to use computational models to assess the image quality consistently with subjective evaluations. This paper proposes a new metric composed of weighted wavelet multi-scale structural similarity (WWMS-SSIM). Four-level 2-D wavelet decomposition is performed for the original and disturbed images, respectively. Each image can be partitioned into one low-frequency subband (LL) and a series of octave high-pass subbands (HL, LH and HH). Different subbands are processed with different weighting factors. Based on the results of the above, we can construct a modified WWMS-SSIM. Comparison experiments show that the correlation, prediction accuracy and consistency of the proposed metric are respectively 5.8%, 5.2% and 4.8% higher than the PSNR metric. The correlation, prediction accuracy and consistency of the proposed metric are respectively 0.7%, 1.6% and 2.6% higher than the SSIM metric. In terms of the experiment results, the WWMS-SSIM metric shows good feasibility comparing with PSNR and SSIM methods.