Divisive Normalization Image Quality Metric Revisited

Valero Laparra, Jordi Muñoz, Jesús Malo
JOSA A, 27(4): 852-864 (2010). Also selected by the OSA for the Virtual Journal for Biomedical Optics 5(8), 2010

Abstract

Structural similarity metrics and information theory based metrics have been proposed as a completely different alternative to the traditional metrics based on error visibility and human vision models. Three basic criticisms were raised against the traditional error visibility approach: (1) it is based on near threshold performance, (2) its geometric meaning may be limited, and (3) stationary pooling strategies may not be statistically justified. These criticisms and the good performance of structural and information theory based metrics have popularized the idea of their superiority over the error visibility approach.

In this work we experimentally or analytically show that the above criticisms do not apply to error visibility metrics that use a general enough Divisive Normalization masking model. According to this, the traditional Divisive Normalization metric [Teo&Heeger 1994] is not intrinsically inferior to the newer approaches. In fact, experiments on a number of databases including a wide range of distortions show that Divisive Normalization is fairly competitive with the newer approaches, robust, and easy to interpret in linear terms.

These results suggest that, despite the criticisms to the traditional error visibility approach, Divisive Normalization masking models should be considered in the image quality discussion.

Key Words: Subjective image fidelity. Non-linear perception model. Divisive Normalization. MSE. SSIM. VIF

References: 55