A Frequency Based Hierarchical Fast Search Block Matching Algorithm for Fast Video Communication

Abstract

Abstract-Numerous fast-search block motion estimation algorithms have been developed to circumvent the high computational cost required by the full-search algorithm. These techniques however often converge to a local minimum, which makes them subject to noise and matching errors. Hence, many spatial domain block matching algorithms have been developed in literature. These algorithms exploit the high correlation that exists between pixels inside each frame block. However, with the block transformed frequencies, block matching can be used to test the similarities between a subset of selected frequencies that correctly identify each block uniquely; therefore fewer comparisons are performed resulting in a considerable reduction in complexity. In this work, a two-level hierarchical fast search motion estimation algorithm is proposed in the frequency domain. This algorithm incorporates a novel search pattern at the top level of the hierarchy. The proposed hierarchical method for motion estimation not only produces consistent motion vectors within each large object, but also accurately estimates the motion of small objects with a substantial reduction in complexity when compared to other benchmark algorithms.

High Performance Block Matching Algorithm for High Bit-Rate Real-Time Video Communication

“…Using the first set of test videos (CIF), and as can be seen in Table 1, the proposed work outperforms the standard three-step search [13], two-dimensional logarithmic search [10] and the diamond search algorithm [23] with 22%, 28% and 23% average enhancement, respectively. Moreover, when compared to the well-know KSHS algorithm [20], ETSS [12], CDMHS [14] and FHFS [30], the proposed algorithm outperforms those algorithms with 12%, 16%, 7% and 2%, respectively.…”

“…Moreover, when compared to the well-know KSHS algorithm [20], ETSS [12], CDMHS [14] and FHFS [30], the proposed algorithm outperforms those algorithms with 11%, 10%, 9% and 5%, respectively. Figure 5 provides a visual representation of the average PSNR values shown in Table 1 and Table 2 and represents the results of the proposed algorithm compared to the rest of the state-of-the-art algorithms when applied to HD (1080p) highresolution videos and normal CIF standard videos.…”

Image subset communication for resource-constrained applications in wireless sensor networks

“…Image and video communications have already surpassed other types of data communication and are the dominant traffic on the Internet [1]. Recently, there has been widespread growth and interest in the deployment and use of visual sensor nodes (VSNs) [2,3].…”