Objective object segmentation visual quality evaluation based on pixel-level and region-level characteristics

Abstract

Objective object segmentation visual quality evaluation is an emergent member of the visual quality assessment family. It aims at developing an objective measure instead of a subjective survey to evaluate the object segmentation quality in agreement with human visual perception. It is an important benchmark to assess and compare performances of object segmentation methods in terms of the visual quality. In spite of its essential role, it still lacks of sufficient studying compared with other visual quality evaluation researches. In this paper, we propose a novel full-reference objective measure including a pixel-level sub-measure and a region-level sub-measure. For the pixel-level sub-measure, it assigns proper weights to not only false positive pixels and false negative pixels but also true positive pixels according to their certainty degrees. For the region-level sub-measure, it considers location distribution of the false negative errors and correlations among neighboring pixels. Thus, by combining these two sub-measures, our measure can evaluate similarity of area, shape and object completeness between one segmentation result and its ground truth in terms of human visual perception. In order to evaluate the performance of our proposed measure, we tested it on an object segmentation subjective visual quality assessment database. The experimental results demonstrate that our proposed measure with good robustness performs better in matching subjective assessments compared with other state-of-the-art objective measures.