Feature comparison and analysis for new challenging research fields of image quality assessment

Abstract

Nowadays, objective image quality assessment (IQA) issue becomes increasingly important for both the practical application and scientific research in digital image processing systems. In this paper, we conduct feature comparison and analysis on new challenging research fields including contrast-distorted, screen content, multiply-distorted, tone-mapped, Depth Image Based Rendering (DIBR) and authentically distorted IQA. We describe the performance criteria, design rationale, and benchmark databases for validating IQA metrics in accordance with human perceptions. Then we provide some important conclusions of feature selection in terms of different IQA problems. In this work, our goal is dominantly towards judging the robustness of mainstream features used in the IQA field, which can help current and emerging researchers for better and faster grasping the speciality of each of novel IQA directions reported.