Improved Disparity Map Computation on Stereoscopic Streaming Video with Multi-core Parallel Implementation

Abstract

Stereo vision has become an important technical issue in the field of 3D imaging, machine vision, robotics, image analysis, and so on. The depth map extraction from stereo video is a key technology of stereoscopic 3D video requiring stereo correspondence algorithms. This is the matching process of the similarity measure for each disparity value, followed by an aggregation and optimization step. Since it requires a lot of computational power, there are significant speed-performance advantages when exploiting parallel processing available on processors. In this situation, multi-core CPU may allow many parallel programming technologies to be realized in users computing devices. This paper proposes parallel implementations for calculating disparity map using a shared memory programming and exploiting the streaming SIMD extension technology. By doing so, we can take advantage both of the hardware and software features of multi-core processor. For the performance evaluation, we implemented a parallel SAD algorithm with OpenMP and SSE2. Their processing speeds are compared with non parallel version on stereoscopic streaming video. The experimental results show that both technologies have a significant effect on the performance and achieve great improvements on processing speed.

Parallel Implementations of Digital Focus Indices Based on Minimax Search Using Multi-Core Processors

“…Another parallel device is the multi-core processor (MCP), which has stronger but fewer cores than a GPU. All these parallel devices are effective for lowering the computational cost of image processing, and therefore, they have been introduced in machine vision, such as in stereo vision [10], image compression [11], and video analysis [12]. However, the parallel architecture for DFI is rarely discussed even though its processing cost is high [9] [13].…”