Objective similarity metrics for scenic bilevel images

Abstract

This paper proposes new objective similarity metrics for scenic bilevel images, which are images containing natural scenes such as landscapes and portraits. Though percentage error is the most commonly used similarity metric for bilevel images, it is not always consistent with human perception. Based on hypotheses about human perception of bilevel images, this paper proposes new metrics that outperform percentage error in the sense of attaining significantly higher Pearson and Spearman-rank correlation coefficients with respect to subjective ratings. The new metrics include Adjusted Percentage Error, Bilevel Gradient Histogram and Connected Components Comparison. The subjective ratings come from similarity evaluations described in a companion paper. Combinations of these metrics are also proposed, which exploit their complementarity to attain even better performance.