We propose a method for local stereo matching that reduces the number of matching comparisons, which also reduces the running time. By using hierarchical structure with suitable disparity candidate set, the proposed method maintains accuracy of local matching methods. Theoretical analysis and experimental results show that the number of comparisons of the proposed method is independent of the disparity range, while the accuracy is nearly the same as the corresponding local method.

We describe a highly scalable parallel arithmetic coder for Markov inputs suitable for implementation on modern multi-core processors. The algorithm divides the input into interleaved sub-sequences which can be then processed independently on different processing units using LDPC-based Slepian-Wolf coding. Experimental simulations show good scalability of the proposed algorithm while also maintaining good compression performance. Notably, when compared with traditional parallel arithmetic coding, the proposed method maintains a much higher efficiency both respect to the entropy limit as well as in terms of the ability to distribute computations across multiple cores without performance loss.

It attracts many researchers' attention to find a stereo matching algorithm both accurate and fast. Yoon and Kweon's Adaptive Support-Weight(ASW) method is supposed to be a very successful algorithm in both accuracy and speed. However, it is very time consuming for ASW to take a large number of pixels into consideration for computing a disparity. In this paper, we present a stereo matching algorithm based on the ASW method to solve this problem. We introduce a time efficient technique, partial sum, to reduce the information of pixels in a large window into a small one so that disparities can be computed using the large window of pixels with low time complexity. Our method can obtain better representation of local properties of a pixel than the ASW method. Experimental results show that our method produces more accurate disparity maps than ASW method.

In this paper, we propose a frame-level heuristic scheduling parallel emerging Multi-view Video Coding (MVC) using Directed Acyclic Graph (DAG) on Intel multi-core processor. We illustrate the reason to choose heuristic scheduling and formulate the problem. Through defining dependent degree and concurrent degree, we demonstrate why to choose frame as parallel granularity. Experimental results demonstrate the effectiveness and scalability of our parallel MVC.

In this paper, using the Intel multicore architectures and the emerging multiview video coding standard, we introduce a framework for performing analysis, simulation, and evaluation of heuristics scheduling algorithms for implementing computationally intensive algorithms on multicore processors. The framework allows for accurate and quantitative characterization of the performance of dynamic scheduling algorithms for multimedia applications on different multicore processors without actual implementation of the scheduling algorithm and application on the actual platform. Experimental results demonstrate the effectiveness and scalability of our framework.