Hardware-accelerated interactive data visualization for neuroscience in Python.

Large datasets are becoming more and more common in science, particularly in neuroscience where experimental techniques are rapidly evolving. Obtaining interpretable results from raw data can sometimes be done automatically; however, there are numerous situations where there is a need, at all processing stages, to visualize the data in an interactive way. This enables the scientist to gain intuition, discover unexpected patterns, and find guidance about subsequent analysis steps. Existing visualization tools mostly focus on static publication-quality figures and do not support interactive visualization of large datasets. While working on Python software for visualization of neurophysiological data, we developed techniques to leverage the computational power of modern graphics cards for high-performance interactive data visualization. We were able to achieve very high performance despite the interpreted and dynamic nature of Python, by using state-of-the-art, fast libraries such as NumPy, PyOpenGL, and PyTables. We present applications of these methods to visualization of neurophysiological data. We believe our tools will be useful in a broad range of domains, in neuroscience and beyond, where there is an increasing need for scalable and fast interactive visualization

GPU-driven recombination and transformation of YCoCg-R video samples

Common programmable Graphics Processing Units (GPU) are capable of more than just rendering real-time effects for games. They can also be used for image processing and the acceleration of video decoding. This paper describes an extended implementation of the H.264/AVC YCoCg-R to RGB color space transformation on the GPU. Both the color space transformation and recombination of the color samples from a nontrivial data layout are performed by the GPU. Using mid- to high-range GPUs, this extended implementation offers a significant gain in processing speed compared to an existing basic GPU version and an optimized CPU implementation. An ATI X1900 GPU was capable of processing more than 73 high-resolution 1080p YCoCg-R frames per second, which is over twice the speed of the CPU-only transformation using a Pentium D 820

MAVIS: Mobile Acquisition and VISualization - a professional tool for video recording on a mobile platform

Professional video recording is a complex process which often requires expensive cameras and large amounts of ancillary equipment. With the advancement of mobile technologies, cameras on mobile devices have improved to the point where the quality of their output is sometimes comparable to that obtained from a professional video camera and are often used in professional productions. However, tools that allow professional users to access the information they need to control the technical quality of their filming and make an informed decision about what they are recording are missing on mobile platforms. In this paper we present MAVIS (Mobile Acquisition and VISualization) a tool for professional filming on a mobile platform. MAVIS allows users to access information such as colour vectorscope, waveform monitor, false colouring, focus peaking and all other information that is needed to produce high quality professional videos. This is achieved by exploiting the capabilities of modern mobile GPUs though the use of a number of vertex and fragment shaders. Evaluation with professionals in the film industry shows that the app and its functionalities are well received and that the output and usability of the application align with professional standards