S.M.M. de Faria

ECG Signal Compression Based on Dc Equalization and Complexity Sorting

In this brief, we present new preprocessing techniques for electrocardiogram signals, namely, DC equalization and complexity sorting, which when applied can improve current 2-D compression algorithms. The experimental results with signals from the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) database outperform the ones from many state-of-the-art schemes described in the literature.

Motion compensation for very low bit-rate video

Abstract A combination of spatial transformation and image segmentation is used to compensate for non-uniform intensity changes in moving scenes. The method efficiently tracks movements such that the motion vectors alone can be employed to represent a moving object with complex motion. Using fast transformation and interpolation algorithms, it is shown that while the compression efficiency of the presented method is far superior to that of the conventional full search block-matching motion estimation, its computational complexity is still affordable.

Universal image coding using multiscale recurrent patterns and prediction

In this paper we present a new method for image coding that is able to achieve good results over a wide range of image types. This work is based on the multidimensional multiscale parser (MMP) algorithm (M. de Carvalho et al., 2002), allied with an intra frame image predictive coding scheme. MMP has been shown to have, for a large class of image data, including texts, graphics, mixed images and textures, a compression efficiency comparable (and, in several cases, well above) to the one of state-of-the-art encoders. However, for smooth grayscale images, its performance lags behind the one of wavelet-based encoders, as JPEG2000. In this paper we propose a novel encoder using MMP with intra predictive coding, similar to the one used in the H.264/AVC video coding standard. Experimental results show that this method closes the performance gap to JPEG-2000 for smooth images, with PSNR gains of up to 1.5 dB. Yet, it maintains the excellent performance level of the MMP for other types of image data, as text, graphics and compound images, lending it a useful universal character.

Multiscale recurrent pattern image coding with a flexible partition scheme

In this paper we present a new segmentation method for the multidimensional multiscale parser (MMP) algorithm. In previous works we have shown that, for text and compound images, MMP has better compression efficiency than state-of-the-art transform-based encoders like JPEG2000 and H.264/AVC; however, it is still inferior to them for smooth images. In this paper we improve the performance of MMP for smooth images by employing a more flexible block segmentation scheme than the one defined in the original algorithm. The new partition scheme allows MMP to exploit the images structure in a much more adaptive and effective way. Experimental tests have shown consistent performance gains, mainly for smooth images. When employing the new block segmentation scheme, MMP outperforms the state-of-the-art JPEG2000 and H.264/AVC Intra-frame image coding algorithms for both smooth and non-smooth images, at low to medium compression ratios.

On ECG Signal Compression With 1-D Multiscale Recurrent Patterns Allied to Preprocessing Techniques

This paper presents the results of a multiscale pattern-matching-based ECG encoder, which employs simple preprocessing techniques for adapting the input signal. Experiments carried out with records from the Massachusetts Institute of Technology-Beth Israel Hospital database show that the proposed scheme is effective, outperforming some state-of-the-art schemes described in the literature.

Improving multiscale recurrent pattern image coding with enhanced dictionary updating strategies

The multidimensional multiscale parser (MMP) is a lossy multidimensional signal encoder, that uses an adaptive dictionary for approximating the original signal using multiscale recurrent pattern matching. In previous work we have shown the efficiency of MMP for image coding and we have also described new techniques to improve its performance, using predictive coding (MMP-Intra) and innovative strategies for reducing the dictionary redundancy. The combination of these methods for image coding achieves much better results than the state-of-the-art JPEG2000 and H.264/AVC Intra image encoders for text and compound images, but for smooth natural images it still presents small losses. In this work we present a new technique to improve the dictionary adaptation process of the MMP-Intra, based on enhanced updating techniques. Experimental results have showed that, when combined with dictionary growth control methods, this technique achieves consistent image quality gains for all image types. Furthermore we present some methods that eliminate the intrinsic substantial increase of the computational complexity associated with more rapidly growing dictionaries, without compromising the final quality of the decoded image.

Efficient dictionary design for multiscale recurrent pattern image coding

MMP-Intra was recently proposed as a recurrent patterns based image encoder that combines the multidimensional multiscale parser (MMP) algorithm with intra prediction techniques. Our results show that this method is able to achieve considerable gains over state-of-the-art transform-based image encoders for a wide variety of types of images, like text, composed (text and graphics) and texture images, while having performance close to the one of traditional algorithms for smooth images. Because of this universal character, MMP-Intra can be regarded as a viable alternative to transform-based image coding. MMP-Intra uses a multiscale adaptive dictionary to approximate the original data blocks. It is composed of dilations, contractions and concatenations of previously encoded patterns. In this work we present a new method for controlling the dictionary adaptability, in which the dictionary is only updated if a certain distortion criterion is met in the block being encoded. Experimental results show that this scheme is able to consistently outperform the original method, while achieving relevant reductions in its computational complexity

Efficient depth map coding using linear residue approximation and a flexible prediction framework

The importance to develop more efficient 3D and multiview data representation algorithms results from the recent market growth for 3D video equipments and associated services. One of the most investigated formats is video+depth which uses depth image based rendering (DIBR) to combine the information of texture and depth, in order to create an arbitrary number of views in the decoder. Such approach requires that depth information must be accurately encoded. However, methods usually employed to encode texture do not seem to be suitable for depth map coding. In this paper we propose a novel depth map coding algorithm based on the assumption that depth images are piecewise-linear smooth signals. This algorithm is designed to encode sharp edges using a flexible dyadic block segmentation and hierarchical intra-prediction framework. The residual signal from this operation is aggregated into blocks which are approximated using linear modeling functions. Furthermore, the proposed algorithm uses a dictionary that increases the coding efficiency for previously used approximations. Experimental results for depth map coding show that synthesized views using the depth maps encoded by the proposed algorithm present higher PSNR than their counterparts, demonstrating the methods efficiency.

Efficient Recurrent Pattern Matching Video Coding

In this paper, we propose a pattern-matching-based algorithm for video compression. This algorithm, named multidimensional multiscale parser (MMP)-Video, is based on the H.264/AVC video encoder, but uses a pattern-matching paradigm instead of the state-of-the-art transform-quantization-entropy encoding approach. The proposed method adopts the use of multiscale recurrent patterns to compress both spatial and temporal prediction residues, totally replacing the use of transforms and quantization. Experimental results show that the coding performance of MMP-Video is better than the one of H.264/AVC high profile, especially for medium to high bit-rates. The gains range up to 0.7 dB, showing that, in spite of its larger computational complexity, the use of multiscale recurrent pattern matching paradigm deserves being investigated as an alternative for video compression.

B-spline filter banks for very low bit rate coding

In this paper we report on the use of B-spline filters in perfect reconstruction filter banks. For each polynomial order the analysis and synthesis FIR filters are evaluated, considering the error introduced by filter coefficient truncation, coding quantization and the distortion introduced by the filters themselves. It is shown that B-spline filters exhibit higher energy concentration in the lower frequency band than other known filter kernels. This property can be exploited for very low bit rate image coding where the higher frequency bands might be very coarsely quantized. For a two channel subband coding system, the reconstructed image from the low frequency channel of the B-spline coder is compared with other filter banks reported in the literature. The simulation results show that B-spline filters achieve better reconstruction quality of images than their counterparts.