On dictionary adaptation for recurrent pattern image coding.

In this paper, we exploit a recently introduced coding algorithm called multidimensional multiscale parser (MMP) as an alternative to the traditional transform quantization-based methods. MMP uses approximate pattern matching with adaptive multiscale dictionaries that contain concatenations of scaled versions of previously encoded image blocks. We propose the use of predictive coding schemes that modify the source's probability distribution, in order to favour the efficiency of MMP's dictionary adaptation. Statistical conditioning is also used, allowing for an increased coding efficiency of the dictionaries' symbols. New dictionary design methods, that allow for an effective compromise between the introduction of new dictionary elements and the reduction of codebook redundancy, are also proposed. Experimental results validate the proposed techniques by showing consistent improvements in PSNR performance over the original MMP algorithm. When compared with state-of-the-art methods, like JPEG2000 and H.264/AVC, the proposed algorithm achieves relevant gains (up to 6 dB) for nonsmooth images and very competitive results for smooth images. These results strongly suggest that the new paradigm posed by MMP can be regarded as an alternative to the one traditionally used in image coding, for a wide range of image types.

Lossless Compression of Medical Images Using 3-D Predictors.

This paper describes a highly efficient method for lossless compression of volumetric sets of medical images, such as CTs or MRIs. The proposed method, referred to as 3-D-MRP, is based on the principle of minimum rate predictors (MRPs), which is one of the state-of-the-art lossless compression technologies presented in the data compression literature. The main features of the proposed method include the use of 3-D predictors, 3-D-block octree partitioning and classification, volume-based optimization, and support for 16-b-depth images. Experimental results demonstrate the efficiency of the 3-D-MRP algorithm for the compression of volumetric sets of medical images, achieving gains above 15% and 12% for 8- and 16-bit-depth contents, respectively, when compared with JPEG-LS, JPEG2000, CALIC, and HEVC, as well as other proposals based on the MRP algorithm.

Image Coding Using Generalized Predictors Based on Sparsity and Geometric Transformations.

Directional intra prediction plays an important role in current state-of-the-art video coding standards. In directional prediction, neighbouring samples are projected along a specific direction to predict a block of samples. Ultimately, each prediction mode can be regarded as a set of very simple linear predictors, a different one for each pixel of a block. Therefore, a natural question that arises is whether one could use the theory of linear prediction in order to generate intra prediction modes that provide increased coding efficiency. However, such an interpretation of each directional mode as a set of linear predictors is too poor to provide useful insights for their design. In this paper, we introduce an interpretation of directional prediction as a particular case of linear prediction, which uses the first-order linear filters and a set of geometric transformations. This interpretation motivated the proposal of a generalized intra prediction framework, whereby the first-order linear filters are replaced by adaptive linear filters with sparsity constraints. In this context, we investigate the use of efficient sparse linear models, adaptively estimated for each block through the use of different algorithms, such as matching pursuit, least angle regression, least absolute shrinkage and selection operator, or elastic net. The proposed intra prediction framework was implemented and evaluated within the state-of-the-art high efficiency video coding standard. Experiments demonstrated the advantage of this predictive solution, mainly in the presence of images with complex features and textured areas, achieving higher average bitrate savings than other related sparse representation methods proposed in the literature.

Intra Predictive Depth Map Coding Using Flexible Block Partitioning.

A complete encoding solution for efficient intra-based depth map compression is proposed in this paper. The algorithm, denominated predictive depth coding (PDC), was specifically developed to efficiently represent the characteristics of depth maps, mostly composed by smooth areas delimited by sharp edges. At its core, PDC involves a directional intra prediction framework and a straightforward residue coding method, combined with an optimized flexible block partitioning scheme. In order to improve the algorithm in the presence of depth edges that cannot be efficiently predicted by the directional modes, a constrained depth modeling mode, based on explicit edge representation, was developed. For residue coding, a simple and low complexity approach was investigated, using constant and linear residue modeling, depending on the prediction mode. The performance of the proposed intra depth map coding approach was evaluated based on the quality of the synthesized views using the encoded depth maps and original texture views. The experimental tests based on all intra configuration demonstrated the superior rate-distortion performance of PDC, with average bitrate savings of 6%, when compared with the current state-of-the-art intra depth map coding solution present in the 3D extension of a high-efficiency video coding (3D-HEVC) standard. By using view synthesis optimization in both PDC and 3D-HEVC encoders, the average bitrate savings increase to 14.3%. This suggests that the proposed method, without using transform-based residue coding, is an efficient alternative to the current 3D-HEVC algorithm for intra depth map coding.

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 image's 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.

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.

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.