Eduardo A. B. da Silva

Multidimensional signal compression using multiscale recurrent patterns

In this paper we propose a new multidimensional signal lossy compression method based on multiscale recurrent patterns, referred to as multidimensional multiscale parser (MMP). In it, a multidimensional signal is recursively segmented into variable-length vectors, and each segment is encoded using expansions and contractions of vectors in a dictionary. The dictionary is updated while the data is being encoded, using concatenations of expanded and contracted versions of previously encoded vectors. The only data encoded are the segmentation tree and the indexes of the vectors in the dictionary, and therefore no side information is necessary for the dictionary updating. The signal segmentation is carried out through a rate-distortion optimization procedure. A two-dimensional version of the MMP algorithm was implemented and tested with several kinds of image data. We have observed that the proposed dictionary updating procedure is effective in adapting the algorithm to a large variety of image content, lending to it a universal flavor. For text and graphics images, it outperforms the state-of-the-art SPIHT algorithm by more that 3 dB at 0.5 opp, while for mixed document images, containing text, graphics and gray-scale images, by more than 1.5 dB at the same rate. Due to the way the images are segmented, they appear slightly blocky at low rates. We have alleviated this problem by proposing an effective way of reducing the blockiness in the reconstructed image, with no penalty in signal-to-noise ratio performance in most cases. We conclude the paper with a theoretical analysis of the approximate matching of Gaussian vectors using scales, which gives a justification of why approximate multiscale matching is a good option, specially at low rates.

The Compression of Electric Signal Waveforms for Smart Grids: State of the Art and Future Trends

In this paper, we discuss the compression of waveforms obtained from measurements of power system quantities and analyze the reasons why its importance is growing with the advent of smart grid systems. While generation and transmission networks already use a considerable number of automation and measurement devices, a large number of smart monitors and meters are to be deployed in the distribution network to allow broad observability and real-time monitoring. This situation creates new requirements concerning the communication interface, computational intelligence and the ability to process data or signals and also to share information. Therefore, a considerable increase in data exchange and in storage is likely to occur. In this context, one must achieve an efficient use of channel communication bandwidth and a reduced need of storage space for power system data. Here, we review the main compression techniques devised for electric signal waveforms providing an overview of the achievements obtained in the past decades. Additionally, we envision some smart grid scenarios emphasizing open research issues regarding compression of electric signal waveforms. We expect that this paper will contribute to motivate joint research efforts between electrical power system and signal processing communities in the area of signal waveform compression.

Autonomous bathymetry for risk assessment with ROAZ robotic surface vehicle

The use of unmanned marine robotic vehicles in bathymetric surveys is discussed. This paper presents recent results in autonomous bathymetric missions with the ROAZ autonomous surface vehicle. In particular, robotic surface vehicles such as ROAZ provide an efficient tool in risk assessment for shallow water environments and water land interface zones as the near surf zone in marine coast. ROAZ is an ocean capable catamaran for distinct oceanographic missions, and with the goal to fill the gap were other hydrographic surveys vehicles/systems are not compiled to operate, like very shallow water rivers and marine coastline surf zones. Therefore, the use of robotic systems for risk assessment is validated through several missions performed either in river scenario (in a very shallow water conditions) and in marine coastlines.

On the empirical rate-distortion performance of Compressive Sensing

Compressive Sensing (CS) is a new paradigm in signal acquisition and compression that has been attracting the interest of the signal compression community. When it comes to image compression applications, it is relevant to estimate the number of bits required to reach a specific image quality. Although several theoretical results regarding the rate-distortion performance of CS have been published recently, there are not many practical image compression results available. The main goal of this paper is to carry out an empirical analysis of the rate-distortion performance of CS in image compression. We analyze issues such as the minimization algorithm used and the transform employed, as well as the trade-off between number of measurements and quantization error. From the experimental results obtained we highlight the potential and limitations of CS when compared to traditional image compression methods.

Scanned Compound Document Encoding Using Multiscale Recurrent Patterns

In this paper, we propose a new encoder for scanned compound documents, based upon a recently introduced coding paradigm called multidimensional multiscale parser (MMP). MMP uses approximate pattern matching, with adaptive multiscale dictionaries that contain concatenations of scaled versions of previously encoded image blocks. These features give MMP the ability to adjust to the input images characteristics, resulting in high coding efficiencies for a wide range of image types. This versatility makes MMP a good candidate for compound digital document encoding. The proposed algorithm first classifies the image blocks as smooth (texture) and nonsmooth (text and graphics). Smooth and nonsmooth blocks are then compressed using different MMP-based encoders, adapted for encoding either type of blocks. The adaptive use of these two types of encoders resulted in performance gains over the original MMP algorithm, further increasing the performance advantage over the current state-of-the-art image encoders for scanned compound images, without compromising the performance for other image types.

Improving multiscale recurrent pattern image coding with least-squares prediction mode

The Multidimensional Multiscale Parser-based (MMP) image coding algorithm, when combined with flexible partitioning and predictive coding techniques (MMP-FP), provides state-of-the-art performance. In this paper we investigate the use of adaptive least-squares prediction in MMP. The linear prediction coefficients implicitly embed the local texture characteristics, and are computed based on a blocks causal neighborhood (composed of already reconstructed data). Thus, the intra prediction mode is adaptively adjusted according to the local context and no extra overhead is needed for signaling the coefficients. We add this new context-adaptive linear prediction mode to the other MMP prediction modes, that are based on the ones used in H.264/AVC; the best mode is chosen through rate-distortion optimization. Simulation results show that least-squares prediction is able to significantly increase MMP-FPs rate-distortion performance for smooth images, leading to better results than the ones of state-of-theart, transform-based methods. Yet with the addition of least-squares prediction MMP-FP presents no performance loss when used for encoding non-smooth images, such as text and graphics.

Light field HEVC-based image coding using locally linear embedding and self-similarity compensated prediction

Light field imaging is a promising new technology that allows the user not only to change the focus and perspective after taking a picture, as well as to generate 3D content, among other applications. However, light field images are characterized by large amounts of data and there is a lack of coding tools to efficiently encode this type of content. Therefore, this paper proposes the addition of two new prediction tools to the HEVC framework, to improve its coding efficiency. The first tool is based on the local linear embedding-based prediction and the second one is based on the self-similarity compensated prediction. Experimental results show improvements over JPEG and HEVC in terms of average bitrate savings of 71.44% and 31.87%, and average PSNR gains of 4.73dB and 0.89dB, respectively.

Intra-prediction for color image coding using YUV correlation

In this paper we present a new algorithm for chroma prediction in YUV images, based on inter component correlation. Despite the YUV color space transformation for inter component decorrelation, some dependency still exists between the Y, U and V chroma components. This dependency has been previously used to predict the chrominance data from the reconstructed luminance. In this paper we show that a chrominance component can be more efficiently predicted by using the reconstructed data from both the luminance and the remaining chrominance signal. The proposed chroma prediction is implemented and tested using the Multidimensional Multiscale Parser (MMP) image encoding algorithm. It is shown that the new color prediction mode outperforms the originally proposed prediction methods. Furthermore, by using the new color prediction scheme, MMP is consistently better than the state-of-the-art H.264/AVC for coding both for the luminance and the chrominance image components.

Middleware proposals for mobile ad hoc networks

Abstract Mobile Ad Hoc Networks are a very fascinating research topic, mainly due to their self-organized and autonomous nature and the absence of pre-established communication infrastructure requirements. In several scenarios, such as conference meetings, rescue operations and battlefields, these networks are extremely attractive. However, the particularities of MANETs, such as their dynamic topology, lack of infrastructure and decentralized characteristics, make the implementation of complex and flexible applications difficult. To enable the deployment of these applications and extend supporting services, several middleware solutions for MANETs can be found in the literature. Middleware is a software layer typically between the operating system and the distributed applications, transparently providing interoperability, distribution of functionality, scalability, load balancing and fault tolerance for the applications. This paper surveys all middleware solutions for MANETs, describing their operations, presenting a comparison of available functionalities and discussing their qualities and limitations, focused on services as group support, resource discovery, location, and security. Proposed middlewares are subdivided into five categories: tuple space-based, P2P-based, context-based, cross-layer and application-oriented solutions. The classification considered their design strategy and communication model characteristics. Also, all presented middleware solutions are depicted in figures, which facilitate the comparison between them. Further, at the end of each category, a summary table is available, making the overall analysis easier. Finally, the paper identifies open issues and researches directions on the design of middleware solutions for MANETs.

A generic post-deblocking filter for block based image compression algorithms

In this paper we propose a new post-processing deblocking technique that is independent of the compression method used to encode the image. The development of this filter was motivated by the use of Multidimensional Multiscale Parser (MMP) algorithm, a generic lossy and lossless compression method. Since it employs an adaptive block size, it presents some impairments when using the deblocking techniques presented in the literature. This led us to the development of a new and more generic deblocking method, based on total variation and adaptive bilateral filtering. The proposed method was evaluated not only for still images, but also for video sequences, encoded using pattern matching and transform based compression methods. For all cases, both the objective and subjective quality of the reconstructed images were improved, showing the versatility of the proposed technique.