A major obstacle in the appreciation of classical music is that extensive training is required to understand musical structure and compositional techniques toward comprehending the thoughts behind the musical work. In this paper, we propose an innovative visualization solution to reveal the semantic structure in classical orchestral works such that users can gain insights into musical structure and appreciate the beauty of music. We formulate the semantic structure into macrolevel layer interactions, microlevel theme variations, and macro-micro relationships between themes and layers to abstract the complicated construction of a musical composition. The visualization has been applied with success in understanding some classical music works as supported by highly promising user study results with the general audience and very positive feedback from music students and experts, demonstrating its effectiveness in conveying the sophistication and beauty of classical music to novice users with informative and intuitive displays.

The semi-transparent nature of direct volume rendered images is useful to depict layered structures in a volume. However, obtaining a semi-transparent result with the layers clearly revealed is difficult and may involve tedious adjustment on opacity and other rendering parameters. Furthermore, the visual quality of layers also depends on various perceptual factors. In this paper, we propose an auto-correction method for enhancing the perceived quality of the semi-transparent layers in direct volume rendered images. We introduce a suite of new measures based on psychological principles to evaluate the perceptual quality of transparent structures in the rendered images. By optimizing rendering parameters within an adaptive and intuitive user interaction process, the quality of the images is enhanced such that specific user requirements can be met. Experimental results on various datasets demonstrate the effectiveness and robustness of our method.

Volume exploration is an important issue in scientific visualization. Research on volume exploration has been focused on revealing hidden structures in volumetric data. While the information of individual structures or features is useful in practice, spatial relations between structures are also important in many applications and can provide further insights into the data. In this paper, we systematically study the extraction, representation, exploration, and visualization of spatial relations in volumetric data and propose a novel relation-aware visualization pipeline for volume exploration. In our pipeline, various relations in the volume are first defined and measured using region connection calculus (RCC) and then represented using a graph interface called relation graph. With RCC and the relation graph, relation query and interactive exploration can be conducted in a comprehensive and intuitive way. The visualization process is further assisted with relation-revealing viewpoint selection and color and opacity enhancement. We also introduce a quality assessment scheme which evaluates the perception of spatial relations in the rendered images. Experiments on various datasets demonstrate the practical use of our system in exploratory visualization.