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Data videos -- motion graphics that incorporate visualizations -- have been recognized as an effective way to communicate ideas, but creating such video requires both time and expertise, precluding them from being created and streamed live. We introduce DataTV, a system for combining multiple media sources in real time. We validate our work through an expert review involving researchers using the DataTV prototype to create a one-minute data video for their current project. Results show that the new method facilitates rapid creation and enables users to focus on the narrative rather than mechanics of video production., Comment: 16 pages

The emerging practice of data-driven storytelling is framing data using familiar narrative mechanisms such as slideshows, videos, and comics to make even highly complex phenomena understandable. However, current data stories are still not utilizing the full potential of the storytelling domain. One reason for this is that current data-driven storytelling practice does not leverage the full repertoire of media that can be used for storytelling, such as the spoken word, e-learning, and video games. In this paper, we propose a taxonomy focused specifically on media types for the purpose of widening the purview of data-driven storytelling simply by putting more tools into the hands of designers. Using our taxonomy as a generative tool, we also propose three novel storytelling mechanisms, including for live-streaming, gesture-driven oral presentations, and textual reports that dynamically incorporate visual representations.

Tensor product real-valued wavelets have been employed in many applications such as image processing with impressive performance. Though edge singularities are ubiquitous and play a fundamental role in two-dimensional problems, tensor product real-valued wavelets are known to be only sub-optimal since they can only capture edges well along the coordinate axis directions. The dual tree complex wavelet transform (DTCWT), proposed by Kingsbury [16] and further developed by Selesnick et al. [24], is one of the most popular and successful enhancements of the classical tensor product real-valued wavelets. The two-dimensional DTCWT is obtained via tensor product and offers improved directionality with 6 directions. In this paper we shall further enhance the performance of DTCWT for the problem of image denoising. Using framelet-based approach and the notion of discrete affine systems, we shall propose a family of tensor product complex tight framelets TPCTF_n for all integers n>2 with increasing directionality, where n refers to the number of filters in the underlying one-dimensional complex tight framelet filter bank. For dimension two, such tensor product complex tight framelet TPCTF_n offers (n-1)(n-3)/2+4 directions when n is odd, and (n-4)(n+2)/2+6 directions when n is even. In particular, TPCTF_4, which is different to DTCWT in both nature and design, provides an alternative to DTCWT. Indeed, TPCTF_4 behaves quite similar to DTCWT by offering 6 directions in dimension two, employing the tensor product structure, and enjoying slightly less redundancy than DTCWT. When TPCTF_4 is applied to image denoising, its performance is comparable to DTCWT. Moreover, better results on image denoising can be obtained by using TPCTF_6. Moreover, TPCTF_n allows us to further improve DTCWT by using TPCTF_n as the first stage filter bank in DTCWT.

Although tensor product real-valued wavelets have been successfully applied to many high-dimensional problems, they can only capture well edge singularities along the coordinate axis directions. As an alternative and improvement of tensor product real-valued wavelets and dual tree complex wavelet transform, recently tensor product complex tight framelets with increasing directionality have been introduced in [8] and applied to image denoising in [13]. Despite several desirable properties, the directional tensor product complex tight framelets constructed in [8,13] are bandlimited and do not have compact support in the space/time domain. Since compactly supported wavelets and framelets are of great interest and importance in both theory and application, it remains as an unsolved problem whether there exist compactly supported tensor product complex tight framelets with directionality. In this paper, we shall satisfactorily answer this question by proving a theoretical result on directionality of tight framelets and by introducing an algorithm to construct compactly supported complex tight framelets with directionality. Our examples show that compactly supported complex tight framelets with directionality can be easily derived from any given eligible low-pass filters and refinable functions. Several examples of compactly supported tensor product complex tight framelets with directionality have been presented.