Diego Noble

High efficient motion estimation architecture with integrated Motion Compensation and FME support

This paper presents an efficient architecture for Motion Estimation (ME) based on the Diamond Search (DS) Algorithm. This architecture also includes the Motion Compensation (MC) for luminance samples, reusing internal ME results and avoiding the additional external memory accesses for the MC operation. The proposed architecture also generates inputs for a Fractional Motion Estimation (FME) for quarter sample precision, as proposed by the H.264/AVC standard. The main goal of this work is to design a low cost ME architecture for the DS algorithm with reduced number of external memory accesses and ready to be integrated with a fractional interpolator. The designed architecture was synthesized to Altera Stratix 4 family of FPGAs and in the worst case scenario, this architecture is able to process Full HD (1080p) videos in real time.

An Analysis of Centrality Measures for Complex and Social Networks

Measures of complex network analysis, such as vertex centrality, have the potential to unveil existing network patterns and behaviors. They contribute to the understanding of networks and their components by analyzing their structural properties, which makes them useful in several computer science domains and applications. Unfortunately, there is a large number of distinct centrality measures and little is known about their common characteristics in practice. By means of an empirical analysis, we aim at a clear understanding of the main centrality measures available, unveiling their similarities and differences in a large number of distinct social networks. Our experiments show that the vertex centrality measures known as information, eigenvector, subgraph, walk betweenness and betweenness can distinguish vertices in all kinds of networks with a granularity performance at 95%, while other metrics achieved a considerably lower result. In addition, we demonstrate that several pairs of metrics evaluate the vertices in a very similar way, i.e. their correlation coefficient values are above 0.7. This was unexpected, considering that each metric presents a quite distinct theoretical and algorithmic foundation. Our work thus contributes towards the development of a methodology for principled network analysis and evaluation.

The Impact of Centrality on Individual and Collective Performance in Social Problem-Solving Systems

In this paper, we analyze the dependency between centrality and individual performance in socially-inspired problem-solving systems. By means of extensive numerical simulations, we investigate how individual performance in four different models correlate with four different classical centrality measures. Our main result shows that there is a high linear correlation between centrality and individual performance when individuals systematically exploit central positions. In this case, central individuals tend to deviate from the expected majority contribution behavior. Although there is ample evidence about the relevance of centrality in social problem-solving, our work contributes to understand that some measures correlate better with individual performance than others due to individual traits, a position that is gaining strength in recent studies.

A real time HDTV motion estimation architecture for the new MPDS algorithm

This paper presents the architectural design for Motion Estimation (ME) based on the new Multi-Point Diamond Search block matching algorithm. This algorithm reduces the local minima falls in the ME search process, increasing the quality of the ME prediction results. This paper presents a software evaluation about the MPDS which presented an average PSNR gain of 3.57dB and a maximum PSNR gain of 7.86dB when compared with the Diamond Search algorithm. The designed architecture was synthesized to an Altera Stratix 4 FPGA and, in the worst case scenario, this architecture is able to process HDTV 720p videos in real time at 28 frames per second.

Two Novel Algorithms for High Quality Motion Estimation in High Definition Video Sequences

In this paper, we propose two new algorithms for high quality motion estimation in high definition digital videos. Both algorithms are based on the use of random features that guarantee robustness to avoid dropping into a local-minimum. The first algorithm was developed from a simple two stage approach where a random stage is complemented by a greedy stage in a very simple fashion. The second algorithm is based on a more refined class of algorithms called Memetic Network Algorithms where each instance of the search may exchange information with its neighbour instances according to some rules that control the information flow. The proposed algorithms were implemented and tested exclusively with high definition sequences against well known fast algorithms like Diamond Search and Three Step Search. The results show that our algorithms can outperform other algorithms in quality yielding an increment in complexity that may be amortized if resources for a parallel execution are available. Additionally, we provide further evidence that fast algorithms do not perform well in high definition.

Two fast multi-point search algorithms for high quality motion estimation in high resolution videos

In this paper, we present two new algorithms focusing on a high quality fast motion estimation process for high definition video coding. Both algorithms provide more efficiency to avoid falling into local minima in fast motion estimation when compared to diamond search (DS) algorithm. They were called multi-point diamond search (MPDS) and dynamic multi-point diamond search (DMPDS). The multi-point search could be done in a serial or parallel approach. In the parallel approach, the penalties in the performance are minimal near the impressive gain in final quality. The MPDS and DMPDS algorithms were implemented and evaluated on ten HD 1080p video sequences. The results show an average quality gain, in comparison with original DS, about 3.9 dB and 4.55 dB for MPDS and DMPDS respectively.

Investigating a Socially Inspired Heterogeneous System of Problem Solving Agents

Social interactions have recently been used as an inspiration for novel agent-based problem-solving models. Particle Swarm Optimization and Memetic Networks are two such algorithms. Although they draw inspiration from different real-world social systems, they both rely on the concept of a social network to regulate the internal information flow in a structured way. In this paper, we systematically investigate how a heterogeneous population composed of individuals from these two models behave as the system seeks the solution to the benchmark problems. We report on extensive numerical simulations, showing that this heterogeneous model is able to converge faster in two highly multimodal scenarios while being otherwise statistically equivalent to the original homogeneous models. Our results provide supportive evidence for the hypothesis that higher diversity in populations of problem-solvers can be beneficial and also adds a new dimension to previous heterogeneous problem-solving models.