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This paper presents a family of algorithms based on sparse decompositions that detect anomalies in video sequences obtained from slow moving cameras. These algorithms start by computing the union of subspaces that best represents all the frames from a reference (anomaly free) video as a low-rank projection plus a sparse residue. Then, they perform a low-rank representation of a target (possibly anomalous) video by taking advantage of both the union of subspaces and the sparse residue computed from the reference video. Such algorithms provide good detection results while at the same time obviating the need for previous video synchronization. However, this is obtained at the cost of a large computational complexity, which hinders their applicability. Another contribution of this paper approaches this problem by using intrinsic properties of the obtained data representation in order to restrict the search space to the most relevant subspaces, providing computational complexity gains of up to two orders of magnitude. The developed algorithms are shown to cope well with videos acquired in challenging scenarios, as verified by the analysis of 59 videos from the VDAO database that comprises videos with abandoned objects in a cluttered industrial scenario. [ABSTRACT FROM AUTHOR]

In this paper, we use the multidimensional multiscale parser (MMP) algorithm, a recently developed universal lossy compression method, to compress data from electrocardiogram (ECG) signals. The MMP is based on approximate multiscale pattern matching, encoding segments of an input signal using expanded and contracted versions of patterns stored in a dictionary. The dictionary is updated using concatenated and displaced versions of previously encoded segments, therefore MMP builds its own dictionary while the input data is being encoded. The MMP can be easily adapted to compress signals of any number of dimensions, and has been successfully applied to compress two-dimensional (2-D) image data. The quasi-periodic nature of ECG signals makes them suitable for compression using recurrent patterns, like MMP does. However, in order for MMP to be able to efficiently compress ECG signals, several adaptations had to be performed, such as the use of a continuity criterion among segments and the adoption of a prune-join strategy for segmentation. The rate-distortion performance achieved was very good. We show simulation results were MMP performs as well as some of the best encoders in the literature, although at the expense of a high computational complexity. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a new biorthogonal linear-phase wavelet design for image compression. Instead of calculating the prototype filters as spectral factors of a half-band filter, the design is based on the direct optimization of the lowpass analysis filter using an objective function directly related to a perceptual criterion for image compression. This function is defined as the product of the theoretical coding gain and an index called the peak-to-peak ratio, which was shown to have high correlation with perceptual quality. A distinctive feature of the proposed technique is a procedure by which, given a “good” starting filter, “good” filters of longer lengths are generated. The results are excellent, showing a clear improvement in perceptual image quality. Also, we devised a criterion for constraining the coefficients of the filters in order to design wavelets with minimum ringing.