The evolution of depth‐perception visualization technologies, emerging format standardization work and research within the field of multi‐view 3D video and imagery addresses the need for flexible 3D video visualization.
The wide variety of available 3D‐display types and visualization techniques for multi‐view video, as well as the high throughput requirements for high definition video, addresses the need for a real‐time 3D video playback solution that takes advantage of hardware accelerated graphics, while providing a high degree of flexibility through format configuration and cross‐platform interoperability.
A modular component based software solution based on FFmpeg for video demultiplexing and video decoding is proposed,using OpenGL and GLUT for hardware accelerated graphics and POSIX threads for increased CPU utilization.
The solution has been verified to have sufficient throughput in order to display 1080p video at the native video frame rate on the experimental system, which is considered as a standard high‐end desktop PC only using commercial hardware. In order to evaluate the performance of the proposed solution a number of throughput evaluation metrics have been introduced measuring average frame rate as a function of: video bit rate, video resolution and number of views .
The results obtained have indicated that the GPU constitutes the primary bottleneck in a multi‐view lenticular rendering system and that multiview rendering performance is degraded as the number of views is increased.This is a result of the current GPU square matrix texture cache architectures, resulting in texture lookup access times according to random memory access patterns when the number of views is high.
The proposed solution has been identified in order to provide low CPU efficiency, i.e. low CPU hardware utilization and it is recommended to increase performance by investigating the gains of scalable multithreading techniques. It is also recommended to investigate the gains of introducing video frame buffering in video memory or to move more calculations to the CPU in order to increase GPU performance.
Source: Mid Sweden University
Author: Andersson, Håkan
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