Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

Hardware acceleration architectures for MPEG-Based mobile video platforms: a brief overview

This paper presents a brief overview of past and current hardware acceleration (HwA) approaches that have been proposed for the most computationally intensive compression tools of the MPEG-4 standard. These approaches are classified based on their historical evolution and architectural approach. An analysis of both evolutionary and functional classifications is carried out in order to speculate on the possible trends of the HwA architectures to be employed in mobile video platforms

Adaptive segmenting of non-stationary signals

Many data compression techniques rely on the low entropy and/or the large degree of autocorrelation exhibited by stationary signals. In non-stationary signals, however, these characteristics are not constant, resulting in reduced data compression efficiency. An adaptive scheme is developed that divides non-stationary signals into smaller locally stationary segments, thereby improving overall efficiency. Two principal issues arise in implementing this procedure. The first is practical; an exhaustive search of all possible segmentations is in general computationally prohibitive. The concept of dynamic programming is applied to reduce the expense of such a search. The second involves choosing a cost function that is appropriate for a particular compression method. Two cost functions are employed here, one based on entropy and the other on correlation. It is shown that by using an appropriate cost function, an adaptively segmented signal offers better data compression efficiency than an unsegmented or arbitrarily segmented signal

Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs