A New Fast Motion Estimation and Mode Decision algorithm for H.264 Depth Maps encoding in Free Viewpoint TV

In this paper, we consider a scenario where 3D scenes are modeled through a View+Depth representation. This representation is to be used at the rendering side to generate synthetic views for free viewpoint video. The encoding of both type of data (view and depth) is carried out using two H.264/AVC encoders. In this scenario we address the reduction of the encoding complexity of depth data. Firstly, an analysis of the Mode Decision and Motion Estimation processes has been conducted for both view and depth sequences, in order to capture the correlation between them. Taking advantage of this correlation, we propose a fast mode decision and motion estimation algorithm for the depth encoding. Results show that the proposed algorithm reduces the computational burden with a negligible loss in terms of quality of the rendered synthetic views. Quality measurements have been conducted using the Video Quality Metric.

Desarrollo de un prototipo usando como dispositivo de interacción Leap Motion

Leap Motion [1] es un pequeño dispositivo que se coloca frente al monitor, conectado mediante un cable USB al ordenador, capaz de capturar los movimientos de nuestras manos y dedos con alta precisión, además de algunos objetos como pinceles o bolígrafos. El objetivo principal de este trabajo es evaluar las capacidades de este dispositivo y crear un prototipo que sea capaz de grabar y reconocer gestos para que pueda ser fácilmente integrado a cualquier aplicación. Para ello, el prototipo consta de 2 funciones principales: Grabar un movimiento: en el que recojo los datos que nos ofrece el Leap Motion, los proceso y los guardo en un formato específico. Reconocer un gesto: en el que comparo en cada momento el gesto que se está realizando con los gestos grabados mediante un algoritmo que detectara si son similares o no. Este es un resumen básico del prototipo, sin embargo debemos tener en cuenta una serie de requisitos y parámetros para hacerlo más eficiente y personalizable dependiendo de las necesidades del usuario. ---ABSTRACT---Leap Motion [1] is a small device we place in front of the display unit, connected to a USB cable to the computer. It is able to capture the motion of our hands and fingers with high accuracy, as well as some objects such as pens and brushes. This project's main goal is to evaluate the proficiency of the device, and create a prototype that is able to record and recognize gestures in order for it to be easily integrated into any application. For that, the prototype has 2 main functions: Recording a motion: in which I collect the data offered by the Leap Motion, process it and keep it in a specific format. Recognizing a gesture: in which I compare each time the gesture being made with the gestures recorded using an algorithm to detect whether they are similar or not. This is a basic summary of the prototype, but we need to take into consideration a number of requirements and parameters to make it more efficient and customizable depending on the user's needs.